Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Relative to standard model training on labeled images in ImageNet, we here demonstrate that the total number of supervised weight updates can be substantially reduced using three complementary strategies:

The review comment to be evaluated is:

Strengths:
- The paper is clear and well written
- The analysis is thorough and supports the authors' arguments
- The method of initializing weights via clusters of trained kernels is interesting
- The "critical training" method of training only selected layers is also interesting

Weaknesses:
- "Recently, such models have been criticized due to how their learning departs from brain development (Marcus, 2004; Grossberg, 2020; Zador, 2019)." This seems to be the motivation for the work, but those authors don't raise the issues that are addressed in this paper. The Marcus book predates deep learning. Grossberg focuses on catastrophic forgetting. Zador focuses on the sparsity of labelled data in life. I am not sure whether the number of weight updates needed to train deep networks presents a substantial independent concern about their biological plausibility, particularly given the possibility of constant unsupervised learning. If the issue has been raised in the literature, more specific references would help here. If not, I think the authors should argue more specifically that it is a problem, before going on to solve it.
- The cluster-based initialization method seems to be presented as a way of bringing some learning results through a genetic bottleneck, but the biological plausibility isn't obvious. Further discussion of how this relates to and departs from biology would be helpful.
- It's interesting that critical training worked so well, but in the context of the paper's goals, the biological relevance of this method should also be laid out more clearly.

Minor:
- I don't think it was shown how classification performance is affected by the cluster-based initialization method, but it would be nice to see that.
- "37 bits per synapse (Zador, 2019)" This refers to the binary connectivity pattern (an issue of architecture) rather than the weights (which are addressed in this paper).

The evidence to be evaluated is:We evaluated the brain predictivity scores of CORnet-S variants that were trained with a combination of fewer epochs and images.

Similarly, when training until convergence with fractions of the 1.28M total images, 50,000 images are sufficient to obtain high neural scores (80% of full training in V1, V2, V4, IT). Behavioral scores again require more training: half the standard number of labeled images is needed to surpass 80%.

For instance, the fully trained model's 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans (Yarbus, 1967; Gibaldi & Sabatini, 2020).

_Weight Compression_ explores the hypothesis that evolution may have discovered an initialization strategy with improved at-birth representations (relative to current initializations). _WC_ is most likely not how evolution found the at-birth synaptic connections, but shows that with nearly identical capacity, an alternative initialization distribution leads to networks that are more brain-like in their adult state - revealing a new space of possibilities (hypotheses) that should be considered (see Appendix B.4 for more details on biological plausibility).

Training thin down-sampling layers reduces the number of updated synapses while maintaining high brain predictivity

As a proof-of-principle that training a reduced set of layers can retain high performance, we propose a novel thin training technique, which we term _Critical Training (CT: Figure 3A). CT_ updates only the weights in critical layers, instead of updating every single model synapse. In CORnet-S, each of the blocks has one down-sampling layer to produce an area's final representation (Figure 3B). We explore successive variants of applying _CT_ up to a block in the architecture and then training the following blocks, e.g. freezing V1, V2, V4 with critical training of the respective down-sampling layers and additional IT training. The final _CT_ ventral stream model is almost completely frozen and only the synapses generating each cortical area's output are trained.

Compared to standard back-propagation training all the weights, all three approaches (_CT, DT, BN_) reduce the number of trained parameters (Figure 3C). However, while the average score with _DT_ (gray) already drops below 65% with over a quarter of trained parameters remaining and _BN_ drops to 62% with very few parameters, _CT_ (blue) maintains over 75% with only 1.4 out of 52.8 million parameters trained.

By reducing the number of trained parameters, _Critical Training_ also yields engineering benefits with more than 40% of the ImageNet score maintained at \(<3\%\) of parameters trained - a significant improvement over 22% accuracy with _BatchNormNorm_(Frankle et al., 2021) while adding only a small number of additional weights. _CT_ further reduces training time by 30% per epoch.

All three training reduction methods independently minimize the number of supervised synaptic updates required to reach a reasonably high brain predictivity score. Reducing the number of supervised updates minimizes the number of epochs and images (Section 3); _Weight Compression (WC)_ improves the at-birth synaptic connectivity for high initial scores with no training at all (Section 4); and _Critical Training (CT)_ reduces the number of synapses that are updated during training (Section 5). Testing synergies between these strategies, we combined all three methods to build novel models that only require a small number of supervised synaptic updates to reasonably capture the mechanisms of adult ventral visual stream processing and object recognition behavior.

_WC+CT_ yield strong benefits (green numbers) in a regime with few supervised updates, improving by up to 27 percent points when training for only 1 epoch on 1,000 images.

We developed a range of models with neural and behavioral scores approaching those of the current leading model of the adult ventral visual stream as quantified in Brain-Score, while requiring only a fraction of supervised synaptic updates. These models were built by complementarily 1) reducing the number of supervised updates, i.e. training epochs and labeled images; 2) improving the "at birth" distribution of synaptic connectivity; and 3) training only critical synapses at the end of each model area.

We verified favorable transfer to models with similar architectures such as ResNet, but generalization to an already compressed MobileNet was limited (Figure 5A).

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream.

A critical component in more closely modeling primate development is to reduce the dependence on labels altogether. Recent unsupervised approaches are starting to rival the classification performance of supervised models (Caron et al., 2018; Henaff et al., 2019; Zhuang et al., 2020) and combining them with the advances presented here could further reduce the number of synaptic updates.

The changes to model initialization and training presented here serve as a proof-of-principle that models can be changed to more closely align with primate development by reducing training steps with labeled images and improving initialization. It is also possible to achieve high brain predictivity when training only a fraction of weights, but all these models are still far from the actual biological mechanisms. We expect future work in this direction to further close the gap with improved evolutionarily encoded wiring mechanisms and developmental learning rules.

Weight Compression (WC)'s cluster-based initialization generates an "at-birth" network that already captures some useful aspects of the visual inputs in a compressed manner and allows the system to learn faster.

However, independent of _how_ these clusters are determined, WC shows that it is possible to encode certain priors about a system's wiring diagram in a very compressed manner.

Our best model _WC+CT_ benefits from a combination of improved initialization through weight compression, and critical training. Figure 8A shows models with standard initialization and training all weights, but with fewer supervised updates (cf. Figure 1), models that only train down-sampling layers (_CT_), and models that combine critical training with weight compression (_WC+CT_). A model initialized with weight compression achieves (only _WC_) 54% brain predictivity score with 0 supervised synaptic updates. Figure 8B and C show detailed brain predictivity scores, relative to a fully trained model, for models initialized and trained with _WC+CT_ (B) and models initialized with standard Kaiming Normal and training all weights (C) when trained with a range of epochs and labeled images. The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model.

To show the generalization of our approach we applied the weight compression methods to a ResNet-50 (He et al., 2016) and a MobileNet (Howard et al., 2017) (version 1, multiplier 1.0, image size 224) architecture. We do not regenerate sampling distributions or clusters based on the new architectures trained weights, but used the CORnet-S based distributions to sample new weights for the different architectures. Since CORnet-S is inspired by ResNet modules, we applied our critical training approach by training all conv3 layers (equivalent down sampling layers) of ResNet50. For MobileNet we explored various layer mappings.

To transfer our critical training approach, we therefore additionally train the \(1\times 1\) MobileNet layers corresponding to conv3. This training version allows for more training but still reduces the amount of trained parameters by 43% while maintaining 78% of the original score.

Furthermore we disable BatchNorm biases and weights in all transfer models by freezing them to default values.

* KN+CT The _KN_-initialized model trained with _Critical Training_ until convergence, i.e. three downstream layers and the decoder are trained and all other layers remain unchanged.

* **WC+CT** The _WC_-initialized model with _Critical Training_.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 3: Training only critical layers reduces the number of updated synapses while maintaining high brain predictivity. A We could naively reduce the parameters of a fully-trained model by freezing layers from the bottom up, training only the top layers (“Downstream Training DT”; gray box). We instead propose Critical Training (CT) which only trains thin “critical” layers (e.g. downsampling layers, blue box). B CORnet-S circuitry. CT only trains critical layers, leaving the rest frozen. C Naively reducing parameters from standard training (black dot, top right) quickly deteriorates brain predictivity scores (DT, gray line) whereas Critical Training reduces parameters while retaining high scores (blue line, CT). D Like C, but measuring ImageNet score. CT retains nearly half the score with a fraction of parameters.

Input: Figure 4: Benefits of combining training reductions for high brain predictivity with few supervised synaptic updates and limitations of techniques in high-update regimes. A (log x-axis) By reducing updates with a combination of fewer supervised updates (Figure 1), improved initialization WC (Figure 2), and training only down-sampling layers CT (Figure 3), the resulting models (dark blue dots; fewer supervised updates alone in light blue) maintain high brain predictivity scores while requiring only a fraction of supervised synaptic updates compared to standard CORnet-S (black dot, top right). B Comparison between WC-initialized models trained with CT versus standardly initialized models training all weights, when varying training epochs and labeled images. Colors represent their percent point difference in brain predictivity scores. WC+CT improve performance in regimes with few epochs and images, but fall behind in regimes with many labeled images and epochs.

Input: Figure 9: Individual brain benchmark scores for WC+CT model A Individual brain predictivitiy scores of WC+CT models trained with a range of epochs on all images. These models score especially high on V1, V2 and V4 already after one epoch in comparison to a model with standard initialization training all weights. IT and Behavior benchmarks continuously improve over later epochs as well but fall short of a fully trained model. B Like A, but with models trained until convergence on different numbers of labeled images, up to the full dataset of 1.28M images (rightmost points). As in A we see > 80% V1, V2 and V4 scores with only 100,000 images. For comparable IT and behavioral scores, more images are required.

Input: Figure 8: Detailed analysis of WC+CT. A When reducing the number of supervised synaptic updates, adding critical training (dark grey) and adding weight compression initialization (dark blue) both improve scores in brain predictivity at the same number of supervised synaptic updates, in comparison to a model with standard initialization and training all weights (bright blue). B Brain predictivities for the WC+CT model when trained with a range of epochs and labeled images. C Same as B, but for a standardly initialized (KN) model training all weights.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that it is generally well-stated with only minor overstatement. The evidence demonstrates:

1. Reduced epochs/images: Data shows high brain predictivity (80% of full training) with just 50,000 images and few epochs.
2. Weight Compression (WC): Achieves 54% brain predictivity with zero updates.
3. Critical Training (CT): Maintains 75% performance with only 1.4M of 52.8M parameters trained.

The claim accurately states these are "complementary strategies" that "substantially" reduce updates. However, there are limitations noted in the review about biological plausibility and transfer to other architectures (especially MobileNet), which the claim doesn't acknowledge.

<score>2</score>

Justification:
- The evidence strongly supports the main assertion about reduction in supervised weight updates through multiple strategies
- Figures 1, 3, 4, 8, and 9 provide clear quantitative support
- The claim is measured in its language ("can be substantially reduced" rather than making absolute statements)
- Minor overstatement comes from not acknowledging limitations in biological relevance and generalization noted in the review
- The review's concerns about biological plausibility suggest slight qualification of the claim would be warranted


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Relative to standard model training on labeled images in ImageNet, we here demonstrate that the total number of supervised weight updates can be substantially reduced using three complementary strategies:

The review comment to be evaluated is:

### Strength:

First of all, the question addressed in the paper is essential for both the machine learning and neuroscience community and the author aim at addressing in a well-written piece of work that also clearly contextualizes and motivates the issue at hand. This work uses a state-of-the-art neural architecture, instead of a reduced version of models as is often the case in this type of work. The authors also propose a good analysis and clearly lay out their results, although their interpretation is one of the weaknesses of the paper.

### Weakness:

Although it is true that this work has many strengths it is hard to overcome the following weaknesses, which question the motivation of the approach undertaken. First, there exists a set of more principled approaches that have been developed over the last 4-5 years for training ANNs in a biologically plausible way.  Second, the results obtained are very subjective to the authors as the result of the improvement of the score is minor.

Although it is true that we don’t expect the authors to solve how the visual system develops and that this work corresponds to a proof of principle, it is still hard to navigate the plausibility of the assumptions made among all possibilities. On the other hand, there is a large set of work that has been proposed to build biologically plausible deep neural networks starting from clear objective function minimization and enforcing locality of learning rules. Compared to this set of works this one seems more adhoc than principled. This represents an important weakness when addressing the more neuroscience-friendly community.

Indeed the approach proposed is more of a combination of existing approaches rather than a novel idea: the use of training of a subset of layers, different weights initialization based on the distribution of trained weights. It is not clear how this provides new insight to the statistical learning community.

The evidence to be evaluated is:We evaluated the brain predictivity scores of CORnet-S variants that were trained with a combination of fewer epochs and images.

Similarly, when training until convergence with fractions of the 1.28M total images, 50,000 images are sufficient to obtain high neural scores (80% of full training in V1, V2, V4, IT). Behavioral scores again require more training: half the standard number of labeled images is needed to surpass 80%.

For instance, the fully trained model's 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans (Yarbus, 1967; Gibaldi & Sabatini, 2020).

_Weight Compression_ explores the hypothesis that evolution may have discovered an initialization strategy with improved at-birth representations (relative to current initializations). _WC_ is most likely not how evolution found the at-birth synaptic connections, but shows that with nearly identical capacity, an alternative initialization distribution leads to networks that are more brain-like in their adult state - revealing a new space of possibilities (hypotheses) that should be considered (see Appendix B.4 for more details on biological plausibility).

Training thin down-sampling layers reduces the number of updated synapses while maintaining high brain predictivity

As a proof-of-principle that training a reduced set of layers can retain high performance, we propose a novel thin training technique, which we term _Critical Training (CT: Figure 3A). CT_ updates only the weights in critical layers, instead of updating every single model synapse. In CORnet-S, each of the blocks has one down-sampling layer to produce an area's final representation (Figure 3B). We explore successive variants of applying _CT_ up to a block in the architecture and then training the following blocks, e.g. freezing V1, V2, V4 with critical training of the respective down-sampling layers and additional IT training. The final _CT_ ventral stream model is almost completely frozen and only the synapses generating each cortical area's output are trained.

Compared to standard back-propagation training all the weights, all three approaches (_CT, DT, BN_) reduce the number of trained parameters (Figure 3C). However, while the average score with _DT_ (gray) already drops below 65% with over a quarter of trained parameters remaining and _BN_ drops to 62% with very few parameters, _CT_ (blue) maintains over 75% with only 1.4 out of 52.8 million parameters trained.

By reducing the number of trained parameters, _Critical Training_ also yields engineering benefits with more than 40% of the ImageNet score maintained at \(<3\%\) of parameters trained - a significant improvement over 22% accuracy with _BatchNormNorm_(Frankle et al., 2021) while adding only a small number of additional weights. _CT_ further reduces training time by 30% per epoch.

All three training reduction methods independently minimize the number of supervised synaptic updates required to reach a reasonably high brain predictivity score. Reducing the number of supervised updates minimizes the number of epochs and images (Section 3); _Weight Compression (WC)_ improves the at-birth synaptic connectivity for high initial scores with no training at all (Section 4); and _Critical Training (CT)_ reduces the number of synapses that are updated during training (Section 5). Testing synergies between these strategies, we combined all three methods to build novel models that only require a small number of supervised synaptic updates to reasonably capture the mechanisms of adult ventral visual stream processing and object recognition behavior.

_WC+CT_ yield strong benefits (green numbers) in a regime with few supervised updates, improving by up to 27 percent points when training for only 1 epoch on 1,000 images.

We developed a range of models with neural and behavioral scores approaching those of the current leading model of the adult ventral visual stream as quantified in Brain-Score, while requiring only a fraction of supervised synaptic updates. These models were built by complementarily 1) reducing the number of supervised updates, i.e. training epochs and labeled images; 2) improving the "at birth" distribution of synaptic connectivity; and 3) training only critical synapses at the end of each model area.

We verified favorable transfer to models with similar architectures such as ResNet, but generalization to an already compressed MobileNet was limited (Figure 5A).

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream.

A critical component in more closely modeling primate development is to reduce the dependence on labels altogether. Recent unsupervised approaches are starting to rival the classification performance of supervised models (Caron et al., 2018; Henaff et al., 2019; Zhuang et al., 2020) and combining them with the advances presented here could further reduce the number of synaptic updates.

The changes to model initialization and training presented here serve as a proof-of-principle that models can be changed to more closely align with primate development by reducing training steps with labeled images and improving initialization. It is also possible to achieve high brain predictivity when training only a fraction of weights, but all these models are still far from the actual biological mechanisms. We expect future work in this direction to further close the gap with improved evolutionarily encoded wiring mechanisms and developmental learning rules.

Weight Compression (WC)'s cluster-based initialization generates an "at-birth" network that already captures some useful aspects of the visual inputs in a compressed manner and allows the system to learn faster.

However, independent of _how_ these clusters are determined, WC shows that it is possible to encode certain priors about a system's wiring diagram in a very compressed manner.

Our best model _WC+CT_ benefits from a combination of improved initialization through weight compression, and critical training. Figure 8A shows models with standard initialization and training all weights, but with fewer supervised updates (cf. Figure 1), models that only train down-sampling layers (_CT_), and models that combine critical training with weight compression (_WC+CT_). A model initialized with weight compression achieves (only _WC_) 54% brain predictivity score with 0 supervised synaptic updates. Figure 8B and C show detailed brain predictivity scores, relative to a fully trained model, for models initialized and trained with _WC+CT_ (B) and models initialized with standard Kaiming Normal and training all weights (C) when trained with a range of epochs and labeled images. The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model.

To show the generalization of our approach we applied the weight compression methods to a ResNet-50 (He et al., 2016) and a MobileNet (Howard et al., 2017) (version 1, multiplier 1.0, image size 224) architecture. We do not regenerate sampling distributions or clusters based on the new architectures trained weights, but used the CORnet-S based distributions to sample new weights for the different architectures. Since CORnet-S is inspired by ResNet modules, we applied our critical training approach by training all conv3 layers (equivalent down sampling layers) of ResNet50. For MobileNet we explored various layer mappings.

To transfer our critical training approach, we therefore additionally train the \(1\times 1\) MobileNet layers corresponding to conv3. This training version allows for more training but still reduces the amount of trained parameters by 43% while maintaining 78% of the original score.

Furthermore we disable BatchNorm biases and weights in all transfer models by freezing them to default values.

* KN+CT The _KN_-initialized model trained with _Critical Training_ until convergence, i.e. three downstream layers and the decoder are trained and all other layers remain unchanged.

* **WC+CT** The _WC_-initialized model with _Critical Training_.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 3: Training only critical layers reduces the number of updated synapses while maintaining high brain predictivity. A We could naively reduce the parameters of a fully-trained model by freezing layers from the bottom up, training only the top layers (“Downstream Training DT”; gray box). We instead propose Critical Training (CT) which only trains thin “critical” layers (e.g. downsampling layers, blue box). B CORnet-S circuitry. CT only trains critical layers, leaving the rest frozen. C Naively reducing parameters from standard training (black dot, top right) quickly deteriorates brain predictivity scores (DT, gray line) whereas Critical Training reduces parameters while retaining high scores (blue line, CT). D Like C, but measuring ImageNet score. CT retains nearly half the score with a fraction of parameters.

Input: Figure 4: Benefits of combining training reductions for high brain predictivity with few supervised synaptic updates and limitations of techniques in high-update regimes. A (log x-axis) By reducing updates with a combination of fewer supervised updates (Figure 1), improved initialization WC (Figure 2), and training only down-sampling layers CT (Figure 3), the resulting models (dark blue dots; fewer supervised updates alone in light blue) maintain high brain predictivity scores while requiring only a fraction of supervised synaptic updates compared to standard CORnet-S (black dot, top right). B Comparison between WC-initialized models trained with CT versus standardly initialized models training all weights, when varying training epochs and labeled images. Colors represent their percent point difference in brain predictivity scores. WC+CT improve performance in regimes with few epochs and images, but fall behind in regimes with many labeled images and epochs.

Input: Figure 9: Individual brain benchmark scores for WC+CT model A Individual brain predictivitiy scores of WC+CT models trained with a range of epochs on all images. These models score especially high on V1, V2 and V4 already after one epoch in comparison to a model with standard initialization training all weights. IT and Behavior benchmarks continuously improve over later epochs as well but fall short of a fully trained model. B Like A, but with models trained until convergence on different numbers of labeled images, up to the full dataset of 1.28M images (rightmost points). As in A we see > 80% V1, V2 and V4 scores with only 100,000 images. For comparable IT and behavioral scores, more images are required.

Input: Figure 8: Detailed analysis of WC+CT. A When reducing the number of supervised synaptic updates, adding critical training (dark grey) and adding weight compression initialization (dark blue) both improve scores in brain predictivity at the same number of supervised synaptic updates, in comparison to a model with standard initialization and training all weights (bright blue). B Brain predictivities for the WC+CT model when trained with a range of epochs and labeled images. C Same as B, but for a standardly initialized (KN) model training all weights.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim, evidence, and reviewer comments, here is my assessment:

The claim about reducing supervised weight updates through three complementary strategies appears to be well-supported by extensive experimental evidence. The paper demonstrates:

1. Fewer epochs/images can achieve 80% of full training scores in early visual areas
2. Weight Compression (WC) shows benefits at initialization (54% brain predictivity with 0 updates)
3. Critical Training (CT) maintains 75% performance with only 1.4M of 52.8M parameters trained

While the reviewer notes this is more of a combination of existing approaches, the empirical results clearly demonstrate substantial reduction in supervised updates through these complementary methods. The claim is measured and accurately reflects what the evidence shows.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Relative to standard model training on labeled images in ImageNet, we here demonstrate that the total number of supervised weight updates can be substantially reduced using three complementary strategies:

The review comment to be evaluated is:

## Strengths
The paper addresses an important problem in theoretical neuroscience: while trained deep networks appear to be good models of the visual stream, the amount of training they need to get there is incompatible with biology.

Two of the proposed approaches to reduce the amount of training are somewhat straightforward: training for fewer epochs and freezing the amount of trained layers. However, I think the provided results are important as they show good fit to brain data can be achieved without closely following the training procedures of deep learning.

The last approach, weight clustering, is perhaps more interesting to the theoretical neuro community, as it works in the same way as genomically encoding the network (the “at birth” state).

Importantly, the proposed ways to reduce the amount of training are tested not only using ImageNet performance, but also ventral stream data, which is arguably more important for neuroscience.

Overall, the paper presents a novel set of results for an important problem, and supports its claims with a significant number of experiments.

## Weaknesses
As mentioned before, I think the weight compression part is the most valuable one, and therefore it should receive more attention in the paper.
1. Clustering should be better explained in the main text. Especially how the number of clusters was chosen.
2. In the beginning of Sec. 4, you discuss how many bits it would take to encode different networks. I think the same should be done for your experiments. E.g. what is the size of CORnet-S vs. the size of a compressed network?
3. I think similar weight compression techniques have been used before, see http://proceedings.mlr.press/v80/wu18h.html for example
4. In general, it’d be interesting to see how at-birth performance changes with the amount of “genetically encoded” information, i.e. the number of clusters passed through compression. E.g., would doubling the amount of clusters you use drastically improve performance? I’m not expecting this experiment to appear in the paper, but perhaps it’s worth discussing.

The majority of results is limited to one architecture, CORnet-S, which is not a very standard deep learning model. However, I think it is OK as repeating the experiments on something like ResNet50 would be very resource-consuming, and CORnet-S appears to be a better model of the visual stream anyway.

## Recommendation
As the paper addresses an important problem with a novel approach and extensive experiments, I recommend to accept the paper.

## Minor points
In Fig.1, were learning rates tuned individually for each length of training, or were short runs just snapshots of the longer ones? If it’s the latter, I’d imagine you can get better results by cranking up the learning rate and later decreasing it before you hit a plateau (e.g., by spacing out the same three LR decreases within a shorter run). I guess doing it for each point in Fig.1A is too much training, but repeating all points in Fig.1B-C should be possible?

> For instance, the fully trained model’s 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans

I would say that a more correct conclusion is that supervised learning is a bad concept for development, but I also think your results are applicable beyond supervised learning. Basically, you show that reducing the amount of updates in backprop retains good Brain-Score performance. But that probably tells more about SGD dynamics in deep networks rather than a specific loss function, and might easily translate to, say, more realistic self-supervised learning. (I guess you briefly mention this point in the discussion.)

> Abstract

> First, we find that only 2% of supervised updates (epochs and images) are needed to achieve ∼80% of the match to adult ventral stream.

80% of the best performing network -- currently it reads as 80% of absolute performance, which would presumably be 1.0 Brain-Score?

The caption of Fig.3C-D should better explain what exactly is trained in each setting

In Sec.6, you should specify that you used MobileNet V1 and ResNet-50.

The evidence to be evaluated is:We evaluated the brain predictivity scores of CORnet-S variants that were trained with a combination of fewer epochs and images.

Similarly, when training until convergence with fractions of the 1.28M total images, 50,000 images are sufficient to obtain high neural scores (80% of full training in V1, V2, V4, IT). Behavioral scores again require more training: half the standard number of labeled images is needed to surpass 80%.

For instance, the fully trained model's 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans (Yarbus, 1967; Gibaldi & Sabatini, 2020).

_Weight Compression_ explores the hypothesis that evolution may have discovered an initialization strategy with improved at-birth representations (relative to current initializations). _WC_ is most likely not how evolution found the at-birth synaptic connections, but shows that with nearly identical capacity, an alternative initialization distribution leads to networks that are more brain-like in their adult state - revealing a new space of possibilities (hypotheses) that should be considered (see Appendix B.4 for more details on biological plausibility).

Training thin down-sampling layers reduces the number of updated synapses while maintaining high brain predictivity

As a proof-of-principle that training a reduced set of layers can retain high performance, we propose a novel thin training technique, which we term _Critical Training (CT: Figure 3A). CT_ updates only the weights in critical layers, instead of updating every single model synapse. In CORnet-S, each of the blocks has one down-sampling layer to produce an area's final representation (Figure 3B). We explore successive variants of applying _CT_ up to a block in the architecture and then training the following blocks, e.g. freezing V1, V2, V4 with critical training of the respective down-sampling layers and additional IT training. The final _CT_ ventral stream model is almost completely frozen and only the synapses generating each cortical area's output are trained.

Compared to standard back-propagation training all the weights, all three approaches (_CT, DT, BN_) reduce the number of trained parameters (Figure 3C). However, while the average score with _DT_ (gray) already drops below 65% with over a quarter of trained parameters remaining and _BN_ drops to 62% with very few parameters, _CT_ (blue) maintains over 75% with only 1.4 out of 52.8 million parameters trained.

By reducing the number of trained parameters, _Critical Training_ also yields engineering benefits with more than 40% of the ImageNet score maintained at \(<3\%\) of parameters trained - a significant improvement over 22% accuracy with _BatchNormNorm_(Frankle et al., 2021) while adding only a small number of additional weights. _CT_ further reduces training time by 30% per epoch.

All three training reduction methods independently minimize the number of supervised synaptic updates required to reach a reasonably high brain predictivity score. Reducing the number of supervised updates minimizes the number of epochs and images (Section 3); _Weight Compression (WC)_ improves the at-birth synaptic connectivity for high initial scores with no training at all (Section 4); and _Critical Training (CT)_ reduces the number of synapses that are updated during training (Section 5). Testing synergies between these strategies, we combined all three methods to build novel models that only require a small number of supervised synaptic updates to reasonably capture the mechanisms of adult ventral visual stream processing and object recognition behavior.

_WC+CT_ yield strong benefits (green numbers) in a regime with few supervised updates, improving by up to 27 percent points when training for only 1 epoch on 1,000 images.

We developed a range of models with neural and behavioral scores approaching those of the current leading model of the adult ventral visual stream as quantified in Brain-Score, while requiring only a fraction of supervised synaptic updates. These models were built by complementarily 1) reducing the number of supervised updates, i.e. training epochs and labeled images; 2) improving the "at birth" distribution of synaptic connectivity; and 3) training only critical synapses at the end of each model area.

We verified favorable transfer to models with similar architectures such as ResNet, but generalization to an already compressed MobileNet was limited (Figure 5A).

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream.

A critical component in more closely modeling primate development is to reduce the dependence on labels altogether. Recent unsupervised approaches are starting to rival the classification performance of supervised models (Caron et al., 2018; Henaff et al., 2019; Zhuang et al., 2020) and combining them with the advances presented here could further reduce the number of synaptic updates.

The changes to model initialization and training presented here serve as a proof-of-principle that models can be changed to more closely align with primate development by reducing training steps with labeled images and improving initialization. It is also possible to achieve high brain predictivity when training only a fraction of weights, but all these models are still far from the actual biological mechanisms. We expect future work in this direction to further close the gap with improved evolutionarily encoded wiring mechanisms and developmental learning rules.

Weight Compression (WC)'s cluster-based initialization generates an "at-birth" network that already captures some useful aspects of the visual inputs in a compressed manner and allows the system to learn faster.

However, independent of _how_ these clusters are determined, WC shows that it is possible to encode certain priors about a system's wiring diagram in a very compressed manner.

Our best model _WC+CT_ benefits from a combination of improved initialization through weight compression, and critical training. Figure 8A shows models with standard initialization and training all weights, but with fewer supervised updates (cf. Figure 1), models that only train down-sampling layers (_CT_), and models that combine critical training with weight compression (_WC+CT_). A model initialized with weight compression achieves (only _WC_) 54% brain predictivity score with 0 supervised synaptic updates. Figure 8B and C show detailed brain predictivity scores, relative to a fully trained model, for models initialized and trained with _WC+CT_ (B) and models initialized with standard Kaiming Normal and training all weights (C) when trained with a range of epochs and labeled images. The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model.

To show the generalization of our approach we applied the weight compression methods to a ResNet-50 (He et al., 2016) and a MobileNet (Howard et al., 2017) (version 1, multiplier 1.0, image size 224) architecture. We do not regenerate sampling distributions or clusters based on the new architectures trained weights, but used the CORnet-S based distributions to sample new weights for the different architectures. Since CORnet-S is inspired by ResNet modules, we applied our critical training approach by training all conv3 layers (equivalent down sampling layers) of ResNet50. For MobileNet we explored various layer mappings.

To transfer our critical training approach, we therefore additionally train the \(1\times 1\) MobileNet layers corresponding to conv3. This training version allows for more training but still reduces the amount of trained parameters by 43% while maintaining 78% of the original score.

Furthermore we disable BatchNorm biases and weights in all transfer models by freezing them to default values.

* KN+CT The _KN_-initialized model trained with _Critical Training_ until convergence, i.e. three downstream layers and the decoder are trained and all other layers remain unchanged.

* **WC+CT** The _WC_-initialized model with _Critical Training_.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 3: Training only critical layers reduces the number of updated synapses while maintaining high brain predictivity. A We could naively reduce the parameters of a fully-trained model by freezing layers from the bottom up, training only the top layers (“Downstream Training DT”; gray box). We instead propose Critical Training (CT) which only trains thin “critical” layers (e.g. downsampling layers, blue box). B CORnet-S circuitry. CT only trains critical layers, leaving the rest frozen. C Naively reducing parameters from standard training (black dot, top right) quickly deteriorates brain predictivity scores (DT, gray line) whereas Critical Training reduces parameters while retaining high scores (blue line, CT). D Like C, but measuring ImageNet score. CT retains nearly half the score with a fraction of parameters.

Input: Figure 4: Benefits of combining training reductions for high brain predictivity with few supervised synaptic updates and limitations of techniques in high-update regimes. A (log x-axis) By reducing updates with a combination of fewer supervised updates (Figure 1), improved initialization WC (Figure 2), and training only down-sampling layers CT (Figure 3), the resulting models (dark blue dots; fewer supervised updates alone in light blue) maintain high brain predictivity scores while requiring only a fraction of supervised synaptic updates compared to standard CORnet-S (black dot, top right). B Comparison between WC-initialized models trained with CT versus standardly initialized models training all weights, when varying training epochs and labeled images. Colors represent their percent point difference in brain predictivity scores. WC+CT improve performance in regimes with few epochs and images, but fall behind in regimes with many labeled images and epochs.

Input: Figure 9: Individual brain benchmark scores for WC+CT model A Individual brain predictivitiy scores of WC+CT models trained with a range of epochs on all images. These models score especially high on V1, V2 and V4 already after one epoch in comparison to a model with standard initialization training all weights. IT and Behavior benchmarks continuously improve over later epochs as well but fall short of a fully trained model. B Like A, but with models trained until convergence on different numbers of labeled images, up to the full dataset of 1.28M images (rightmost points). As in A we see > 80% V1, V2 and V4 scores with only 100,000 images. For comparable IT and behavioral scores, more images are required.

Input: Figure 8: Detailed analysis of WC+CT. A When reducing the number of supervised synaptic updates, adding critical training (dark grey) and adding weight compression initialization (dark blue) both improve scores in brain predictivity at the same number of supervised synaptic updates, in comparison to a model with standard initialization and training all weights (bright blue). B Brain predictivities for the WC+CT model when trained with a range of epochs and labeled images. C Same as B, but for a standardly initialized (KN) model training all weights.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that the statement about demonstrating substantial reduction in supervised weight updates through three complementary strategies is generally well-supported by the evidence presented. The paper shows:

1. Training with fewer epochs/images achieves ~80% brain predictivity with just 2% of updates
2. Weight Compression (WC) provides 54% brain predictivity with 0 supervised updates
3. Critical Training (CT) maintains 75% performance with only 1.4M of 52.8M parameters trained

The reviewer also confirms these as significant findings, particularly highlighting the importance of weight compression. While some limitations exist (e.g., limited testing on other architectures), the core claim about demonstrating reduction in supervised updates is measured and supported by extensive experimental evidence.

<score>1</score>

Justification:
- Clear quantitative evidence showing reduced updates while maintaining significant performance
- Multiple complementary approaches validated through experiments
- Conservative language ("demonstrate" rather than stronger claims)
- Reviewer confirms the significance and validity of findings
- Limitations are acknowledged in the paper
- Results are thoroughly documented with detailed figures and analyses


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Relative to standard model training on labeled images in ImageNet, we here demonstrate that the total number of supervised weight updates can be substantially reduced using three complementary strategies:

The review comment to be evaluated is:

The authors present thorough analyses of standard feedforward networks that quantitatively demonstrate that such networks can model the visual stream well while requiring far fewer coordinated parameter changes than occur with traditional training methods.  In particular, they show that very little supervision is required for strong matches to neural responses, that plausible distributions of initial random weights can match neural responses very well, and that weights can be updated very sparsely while still achieving quite high neural response matches.

Such findings are highly relevant to allowing for such networks to be more plausibly accepted by the neuroscience community as valid models of cortical networks.  The striking finding that well-chosen random weights (Weight Compression) can predict neural responses with more than 50% accuracy is  especially interesting, as it suggests that indeed there may be less training required in mammalian brains compared to randomly initialized neuronal networks. This possibility opens up further avenues of study relating to the ability of real brains to learn with far fewer examples compared to ANNs.

Figure 4b further demonstrates this possibility, showing that, under the ethologically relevant condition of little supervision, WC + Critical Training, where only a small subset of weights in so-called critical layers are trained, greatly outperforms traditional training.  We note that, in the ideal case, a biologically plausible method might be found that performs well both under little supervision, as WC+CT does here, and under a large amount of supervision, as traditional backprop does, the demonstration of principle for the former case already represents appreciable progress.

However, little in the way of tying model predictivity to accuracy on another task was explored. While tying brain predictivity to network performance is of course a very active area in its own right, we feel that addressing this directly within the context of the present work would facilitate readers' abilities to contextualize the present study with respect to ANN performance.

Overall, we find the authors' presented work to be of great interest to those interested in applying ML models to theoretical questions in neuroscience and to have a thorough set of analyses that both illustrates progress in this area while suggesting future avenues of work.

Minor:
- opening quotes are backwards throughout paper

The evidence to be evaluated is:We evaluated the brain predictivity scores of CORnet-S variants that were trained with a combination of fewer epochs and images.

Similarly, when training until convergence with fractions of the 1.28M total images, 50,000 images are sufficient to obtain high neural scores (80% of full training in V1, V2, V4, IT). Behavioral scores again require more training: half the standard number of labeled images is needed to surpass 80%.

For instance, the fully trained model's 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans (Yarbus, 1967; Gibaldi & Sabatini, 2020).

_Weight Compression_ explores the hypothesis that evolution may have discovered an initialization strategy with improved at-birth representations (relative to current initializations). _WC_ is most likely not how evolution found the at-birth synaptic connections, but shows that with nearly identical capacity, an alternative initialization distribution leads to networks that are more brain-like in their adult state - revealing a new space of possibilities (hypotheses) that should be considered (see Appendix B.4 for more details on biological plausibility).

Training thin down-sampling layers reduces the number of updated synapses while maintaining high brain predictivity

As a proof-of-principle that training a reduced set of layers can retain high performance, we propose a novel thin training technique, which we term _Critical Training (CT: Figure 3A). CT_ updates only the weights in critical layers, instead of updating every single model synapse. In CORnet-S, each of the blocks has one down-sampling layer to produce an area's final representation (Figure 3B). We explore successive variants of applying _CT_ up to a block in the architecture and then training the following blocks, e.g. freezing V1, V2, V4 with critical training of the respective down-sampling layers and additional IT training. The final _CT_ ventral stream model is almost completely frozen and only the synapses generating each cortical area's output are trained.

Compared to standard back-propagation training all the weights, all three approaches (_CT, DT, BN_) reduce the number of trained parameters (Figure 3C). However, while the average score with _DT_ (gray) already drops below 65% with over a quarter of trained parameters remaining and _BN_ drops to 62% with very few parameters, _CT_ (blue) maintains over 75% with only 1.4 out of 52.8 million parameters trained.

By reducing the number of trained parameters, _Critical Training_ also yields engineering benefits with more than 40% of the ImageNet score maintained at \(<3\%\) of parameters trained - a significant improvement over 22% accuracy with _BatchNormNorm_(Frankle et al., 2021) while adding only a small number of additional weights. _CT_ further reduces training time by 30% per epoch.

All three training reduction methods independently minimize the number of supervised synaptic updates required to reach a reasonably high brain predictivity score. Reducing the number of supervised updates minimizes the number of epochs and images (Section 3); _Weight Compression (WC)_ improves the at-birth synaptic connectivity for high initial scores with no training at all (Section 4); and _Critical Training (CT)_ reduces the number of synapses that are updated during training (Section 5). Testing synergies between these strategies, we combined all three methods to build novel models that only require a small number of supervised synaptic updates to reasonably capture the mechanisms of adult ventral visual stream processing and object recognition behavior.

_WC+CT_ yield strong benefits (green numbers) in a regime with few supervised updates, improving by up to 27 percent points when training for only 1 epoch on 1,000 images.

We developed a range of models with neural and behavioral scores approaching those of the current leading model of the adult ventral visual stream as quantified in Brain-Score, while requiring only a fraction of supervised synaptic updates. These models were built by complementarily 1) reducing the number of supervised updates, i.e. training epochs and labeled images; 2) improving the "at birth" distribution of synaptic connectivity; and 3) training only critical synapses at the end of each model area.

We verified favorable transfer to models with similar architectures such as ResNet, but generalization to an already compressed MobileNet was limited (Figure 5A).

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream.

A critical component in more closely modeling primate development is to reduce the dependence on labels altogether. Recent unsupervised approaches are starting to rival the classification performance of supervised models (Caron et al., 2018; Henaff et al., 2019; Zhuang et al., 2020) and combining them with the advances presented here could further reduce the number of synaptic updates.

The changes to model initialization and training presented here serve as a proof-of-principle that models can be changed to more closely align with primate development by reducing training steps with labeled images and improving initialization. It is also possible to achieve high brain predictivity when training only a fraction of weights, but all these models are still far from the actual biological mechanisms. We expect future work in this direction to further close the gap with improved evolutionarily encoded wiring mechanisms and developmental learning rules.

Weight Compression (WC)'s cluster-based initialization generates an "at-birth" network that already captures some useful aspects of the visual inputs in a compressed manner and allows the system to learn faster.

However, independent of _how_ these clusters are determined, WC shows that it is possible to encode certain priors about a system's wiring diagram in a very compressed manner.

Our best model _WC+CT_ benefits from a combination of improved initialization through weight compression, and critical training. Figure 8A shows models with standard initialization and training all weights, but with fewer supervised updates (cf. Figure 1), models that only train down-sampling layers (_CT_), and models that combine critical training with weight compression (_WC+CT_). A model initialized with weight compression achieves (only _WC_) 54% brain predictivity score with 0 supervised synaptic updates. Figure 8B and C show detailed brain predictivity scores, relative to a fully trained model, for models initialized and trained with _WC+CT_ (B) and models initialized with standard Kaiming Normal and training all weights (C) when trained with a range of epochs and labeled images. The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model.

To show the generalization of our approach we applied the weight compression methods to a ResNet-50 (He et al., 2016) and a MobileNet (Howard et al., 2017) (version 1, multiplier 1.0, image size 224) architecture. We do not regenerate sampling distributions or clusters based on the new architectures trained weights, but used the CORnet-S based distributions to sample new weights for the different architectures. Since CORnet-S is inspired by ResNet modules, we applied our critical training approach by training all conv3 layers (equivalent down sampling layers) of ResNet50. For MobileNet we explored various layer mappings.

To transfer our critical training approach, we therefore additionally train the \(1\times 1\) MobileNet layers corresponding to conv3. This training version allows for more training but still reduces the amount of trained parameters by 43% while maintaining 78% of the original score.

Furthermore we disable BatchNorm biases and weights in all transfer models by freezing them to default values.

* KN+CT The _KN_-initialized model trained with _Critical Training_ until convergence, i.e. three downstream layers and the decoder are trained and all other layers remain unchanged.

* **WC+CT** The _WC_-initialized model with _Critical Training_.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 3: Training only critical layers reduces the number of updated synapses while maintaining high brain predictivity. A We could naively reduce the parameters of a fully-trained model by freezing layers from the bottom up, training only the top layers (“Downstream Training DT”; gray box). We instead propose Critical Training (CT) which only trains thin “critical” layers (e.g. downsampling layers, blue box). B CORnet-S circuitry. CT only trains critical layers, leaving the rest frozen. C Naively reducing parameters from standard training (black dot, top right) quickly deteriorates brain predictivity scores (DT, gray line) whereas Critical Training reduces parameters while retaining high scores (blue line, CT). D Like C, but measuring ImageNet score. CT retains nearly half the score with a fraction of parameters.

Input: Figure 4: Benefits of combining training reductions for high brain predictivity with few supervised synaptic updates and limitations of techniques in high-update regimes. A (log x-axis) By reducing updates with a combination of fewer supervised updates (Figure 1), improved initialization WC (Figure 2), and training only down-sampling layers CT (Figure 3), the resulting models (dark blue dots; fewer supervised updates alone in light blue) maintain high brain predictivity scores while requiring only a fraction of supervised synaptic updates compared to standard CORnet-S (black dot, top right). B Comparison between WC-initialized models trained with CT versus standardly initialized models training all weights, when varying training epochs and labeled images. Colors represent their percent point difference in brain predictivity scores. WC+CT improve performance in regimes with few epochs and images, but fall behind in regimes with many labeled images and epochs.

Input: Figure 9: Individual brain benchmark scores for WC+CT model A Individual brain predictivitiy scores of WC+CT models trained with a range of epochs on all images. These models score especially high on V1, V2 and V4 already after one epoch in comparison to a model with standard initialization training all weights. IT and Behavior benchmarks continuously improve over later epochs as well but fall short of a fully trained model. B Like A, but with models trained until convergence on different numbers of labeled images, up to the full dataset of 1.28M images (rightmost points). As in A we see > 80% V1, V2 and V4 scores with only 100,000 images. For comparable IT and behavioral scores, more images are required.

Input: Figure 8: Detailed analysis of WC+CT. A When reducing the number of supervised synaptic updates, adding critical training (dark grey) and adding weight compression initialization (dark blue) both improve scores in brain predictivity at the same number of supervised synaptic updates, in comparison to a model with standard initialization and training all weights (bright blue). B Brain predictivities for the WC+CT model when trained with a range of epochs and labeled images. C Same as B, but for a standardly initialized (KN) model training all weights.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review of the evidence and reviewer comments, I'll assess the claim that "the total number of supervised weight updates can be substantially reduced using three complementary strategies."

The evidence strongly supports this claim through:
1. Quantitative results showing high brain predictivity (80% of full training) with just 50,000 images vs 1.28M standard
2. Weight Compression (WC) achieving 54% brain predictivity with zero updates
3. Critical Training (CT) maintaining 75% performance with only 1.4M vs 52.8M parameters
4. Combined WC+CT showing 27% improvement with minimal training

The claim is carefully worded, acknowledging these are "strategies" rather than biological mechanisms, and uses measured language like "can be reduced" rather than making absolute statements. The reviewer comments align with this assessment, noting the "thorough analyses" while acknowledging limitations.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

First, we find that only 2% of supervised updates (epochs and images) are needed to achieve \(\sim\)80% of a fully trained model's match to adult ventral stream.

The evidence to be evaluated is:We evaluated the brain predictivity scores of CORnet-S variants that were trained with a combination of fewer epochs and images.

Similarly, when training until convergence with fractions of the 1.28M total images, 50,000 images are sufficient to obtain high neural scores (80% of full training in V1, V2, V4, IT).

For instance, the fully trained model's 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans (Yarbus, 1967; Gibaldi & Sabatini, 2020).

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience. Training thin down-sampling layers reduces the number of updated synapses while maintaining high brain predictivity

As a proof-of-principle that training a reduced set of layers can retain high performance, we propose a novel thin training technique, which we term _Critical Training (CT: Figure 3A). CT_ updates only the weights in critical layers, instead of updating every single model synapse.

We explore successive variants of applying _CT_ up to a block in the architecture and then training the following blocks, e.g. freezing V1, V2, V4 with critical training of the respective down-sampling layers and additional IT training.

However, while the average score with _DT_ (gray) already drops below 65% with over a quarter of trained parameters remaining and _BN_ drops to 62% with very few parameters, _CT_ (blue) maintains over 75% with only 1.4 out of 52.8 million parameters trained.

By reducing the number of trained parameters, _Critical Training_ also yields engineering benefits with more than 40% of the ImageNet score maintained at \(<3\%\) of parameters trained - a significant improvement over 22% accuracy with _BatchNormNorm_(Frankle et al., 2021) while adding only a small number of additional weights.

High brain predictivity can be achieved with a relatively small number of supervised synaptic updates All three training reduction methods independently minimize the number of supervised synaptic updates required to reach a reasonably high brain predictivity score. Reducing the number of supervised updates minimizes the number of epochs and images (Section 3); _Weight Compression (WC)_ improves the at-birth synaptic connectivity for high initial scores with no training at all (Section 4); and _Critical Training (CT)_ reduces the number of synapses that are updated during training (Section 5). Testing synergies between these strategies, we combined all three methods to build novel models that only require a small number of supervised synaptic updates to reasonably capture the mechanisms of adult ventral visual stream processing and object recognition behavior.

_WC+CT_ yield strong benefits (green numbers) in a regime with few supervised updates, improving by up to 27 percent points when training for only 1 epoch on 1,000 images.

_WC+CT_ therefore most positively interact with a small budget of supervised updates (which is the focus of this work).

We developed a range of models with neural and behavioral scores approaching those of the current leading model of the adult ventral visual stream as quantified in Brain-Score, while requiring only a fraction of supervised synaptic updates. These models were built by complementarily 1) reducing the number of supervised updates, i.e. training epochs and labeled images; 2) improving the "at birth" distribution of synaptic connectivity; and 3) training only critical synapses at the end of each model area.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

Recent unsupervised approaches are starting to rival the classification performance of supervised models (Caron et al., 2018; Henaff et al., 2019; Zhuang et al., 2020) and combining them with the advances presented here could further reduce the number of synaptic updates.

The changes to model initialization and training presented here serve as a proof-of-principle that models can be changed to more closely align with primate development by reducing training steps with labeled images and improving initialization. It is also possible to achieve high brain predictivity when training only a fraction of weights, but all these models are still far from the actual biological mechanisms.

Weight Compression (WC)'s cluster-based initialization generates an "at-birth" network that already captures some useful aspects of the visual inputs in a compressed manner and allows the system to learn faster.

Our best model _WC+CT_ benefits from a combination of improved initialization through weight compression, and critical training. Figure 8A shows models with standard initialization and training all weights, but with fewer supervised updates (cf. Figure 1), models that only train down-sampling layers (_CT_), and models that combine critical training with weight compression (_WC+CT_).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 4: Benefits of combining training reductions for high brain predictivity with few supervised synaptic updates and limitations of techniques in high-update regimes. A (log x-axis) By reducing updates with a combination of fewer supervised updates (Figure 1), improved initialization WC (Figure 2), and training only down-sampling layers CT (Figure 3), the resulting models (dark blue dots; fewer supervised updates alone in light blue) maintain high brain predictivity scores while requiring only a fraction of supervised synaptic updates compared to standard CORnet-S (black dot, top right). B Comparison between WC-initialized models trained with CT versus standardly initialized models training all weights, when varying training epochs and labeled images. Colors represent their percent point difference in brain predictivity scores. WC+CT improve performance in regimes with few epochs and images, but fall behind in regimes with many labeled images and epochs.

Input: Figure 8: Detailed analysis of WC+CT. A When reducing the number of supervised synaptic updates, adding critical training (dark grey) and adding weight compression initialization (dark blue) both improve scores in brain predictivity at the same number of supervised synaptic updates, in comparison to a model with standard initialization and training all weights (bright blue). B Brain predictivities for the WC+CT model when trained with a range of epochs and labeled images. C Same as B, but for a standardly initialized (KN) model training all weights.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review, the claim that "only 2% of supervised updates are needed to achieve ~80% of a fully trained model's match to adult ventral stream" appears to be slightly overstated based on the evidence. Looking at Figure 1A, while there is indeed a point showing around 80% performance with significantly reduced updates, the exact 2% figure isn't clearly demonstrated. Figure 4A shows a more nuanced progression, with various models achieving different performance levels at different update percentages. The claim simplifies a more complex relationship between updates and performance.

The evidence does support that substantial brain predictivity can be achieved with reduced updates, but the specific "2%" and "80%" numbers seem to be approximations that don't fully capture the nuanced findings presented across multiple experiments and conditions.

<score>3</score>

Justification:
1. The evidence shows reduced updates can achieve good performance, but the exact 2%->80% relationship isn't definitively proven
2. Figures 1 and 4 show a gradual improvement curve rather than a clear 80% threshold at 2% updates
3. The paper presents multiple approaches (WC, CT, combined methods) with varying results, making a single percentage claim oversimplified
4. The evidence demonstrates this is highly dependent on which visual areas are being measured and what training approach is used
5. The claim would be more accurate if it acknowledged these variations and specified which training method and visual areas it refers to


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

First, we find that only 2% of supervised updates (epochs and images) are needed to achieve \(\sim\)80% of a fully trained model's match to adult ventral stream.

The review comment to be evaluated is:

Strengths:
- The paper is clear and well written
- The analysis is thorough and supports the authors' arguments
- The method of initializing weights via clusters of trained kernels is interesting
- The "critical training" method of training only selected layers is also interesting

Weaknesses:
- "Recently, such models have been criticized due to how their learning departs from brain development (Marcus, 2004; Grossberg, 2020; Zador, 2019)." This seems to be the motivation for the work, but those authors don't raise the issues that are addressed in this paper. The Marcus book predates deep learning. Grossberg focuses on catastrophic forgetting. Zador focuses on the sparsity of labelled data in life. I am not sure whether the number of weight updates needed to train deep networks presents a substantial independent concern about their biological plausibility, particularly given the possibility of constant unsupervised learning. If the issue has been raised in the literature, more specific references would help here. If not, I think the authors should argue more specifically that it is a problem, before going on to solve it.
- The cluster-based initialization method seems to be presented as a way of bringing some learning results through a genetic bottleneck, but the biological plausibility isn't obvious. Further discussion of how this relates to and departs from biology would be helpful.
- It's interesting that critical training worked so well, but in the context of the paper's goals, the biological relevance of this method should also be laid out more clearly.

Minor:
- I don't think it was shown how classification performance is affected by the cluster-based initialization method, but it would be nice to see that.
- "37 bits per synapse (Zador, 2019)" This refers to the binary connectivity pattern (an issue of architecture) rather than the weights (which are addressed in this paper).

The evidence to be evaluated is:We evaluated the brain predictivity scores of CORnet-S variants that were trained with a combination of fewer epochs and images.

Similarly, when training until convergence with fractions of the 1.28M total images, 50,000 images are sufficient to obtain high neural scores (80% of full training in V1, V2, V4, IT).

For instance, the fully trained model's 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans (Yarbus, 1967; Gibaldi & Sabatini, 2020).

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience. Training thin down-sampling layers reduces the number of updated synapses while maintaining high brain predictivity

As a proof-of-principle that training a reduced set of layers can retain high performance, we propose a novel thin training technique, which we term _Critical Training (CT: Figure 3A). CT_ updates only the weights in critical layers, instead of updating every single model synapse.

We explore successive variants of applying _CT_ up to a block in the architecture and then training the following blocks, e.g. freezing V1, V2, V4 with critical training of the respective down-sampling layers and additional IT training.

However, while the average score with _DT_ (gray) already drops below 65% with over a quarter of trained parameters remaining and _BN_ drops to 62% with very few parameters, _CT_ (blue) maintains over 75% with only 1.4 out of 52.8 million parameters trained.

By reducing the number of trained parameters, _Critical Training_ also yields engineering benefits with more than 40% of the ImageNet score maintained at \(<3\%\) of parameters trained - a significant improvement over 22% accuracy with _BatchNormNorm_(Frankle et al., 2021) while adding only a small number of additional weights.

High brain predictivity can be achieved with a relatively small number of supervised synaptic updates All three training reduction methods independently minimize the number of supervised synaptic updates required to reach a reasonably high brain predictivity score. Reducing the number of supervised updates minimizes the number of epochs and images (Section 3); _Weight Compression (WC)_ improves the at-birth synaptic connectivity for high initial scores with no training at all (Section 4); and _Critical Training (CT)_ reduces the number of synapses that are updated during training (Section 5). Testing synergies between these strategies, we combined all three methods to build novel models that only require a small number of supervised synaptic updates to reasonably capture the mechanisms of adult ventral visual stream processing and object recognition behavior.

_WC+CT_ yield strong benefits (green numbers) in a regime with few supervised updates, improving by up to 27 percent points when training for only 1 epoch on 1,000 images.

_WC+CT_ therefore most positively interact with a small budget of supervised updates (which is the focus of this work).

We developed a range of models with neural and behavioral scores approaching those of the current leading model of the adult ventral visual stream as quantified in Brain-Score, while requiring only a fraction of supervised synaptic updates. These models were built by complementarily 1) reducing the number of supervised updates, i.e. training epochs and labeled images; 2) improving the "at birth" distribution of synaptic connectivity; and 3) training only critical synapses at the end of each model area.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

Recent unsupervised approaches are starting to rival the classification performance of supervised models (Caron et al., 2018; Henaff et al., 2019; Zhuang et al., 2020) and combining them with the advances presented here could further reduce the number of synaptic updates.

The changes to model initialization and training presented here serve as a proof-of-principle that models can be changed to more closely align with primate development by reducing training steps with labeled images and improving initialization. It is also possible to achieve high brain predictivity when training only a fraction of weights, but all these models are still far from the actual biological mechanisms.

Weight Compression (WC)'s cluster-based initialization generates an "at-birth" network that already captures some useful aspects of the visual inputs in a compressed manner and allows the system to learn faster.

Our best model _WC+CT_ benefits from a combination of improved initialization through weight compression, and critical training. Figure 8A shows models with standard initialization and training all weights, but with fewer supervised updates (cf. Figure 1), models that only train down-sampling layers (_CT_), and models that combine critical training with weight compression (_WC+CT_).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 4: Benefits of combining training reductions for high brain predictivity with few supervised synaptic updates and limitations of techniques in high-update regimes. A (log x-axis) By reducing updates with a combination of fewer supervised updates (Figure 1), improved initialization WC (Figure 2), and training only down-sampling layers CT (Figure 3), the resulting models (dark blue dots; fewer supervised updates alone in light blue) maintain high brain predictivity scores while requiring only a fraction of supervised synaptic updates compared to standard CORnet-S (black dot, top right). B Comparison between WC-initialized models trained with CT versus standardly initialized models training all weights, when varying training epochs and labeled images. Colors represent their percent point difference in brain predictivity scores. WC+CT improve performance in regimes with few epochs and images, but fall behind in regimes with many labeled images and epochs.

Input: Figure 8: Detailed analysis of WC+CT. A When reducing the number of supervised synaptic updates, adding critical training (dark grey) and adding weight compression initialization (dark blue) both improve scores in brain predictivity at the same number of supervised synaptic updates, in comparison to a model with standard initialization and training all weights (bright blue). B Brain predictivities for the WC+CT model when trained with a range of epochs and labeled images. C Same as B, but for a standardly initialized (KN) model training all weights.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, I find that the claim "only 2% of supervised updates (epochs and images) are needed to achieve ~80% of a fully trained model's match to adult ventral stream" is quite well-supported by the data, particularly in Figure 1A which clearly shows this relationship. The evidence demonstrates that with just 2% of updates, models achieve approximately 76-80% of full brain predictivity scores. This is further corroborated by detailed analyses showing how reducing training epochs and images still maintains high brain predictivity, especially in early visual areas.

The claim is precise and conservative in its wording, using "~80%" to acknowledge some variation, and is directly grounded in the quantitative evidence presented. It doesn't overextend beyond what the data shows.

<score>1</score>

Justification:
1. Direct Evidence Support:
- Figure 1A explicitly shows the 2% updates → ~80% score relationship
- Multiple experiments validate this finding through different methods (critical training, weight compression)
- Detailed benchmark scores in Figure 9 confirm these results

2. Careful Wording:
- Uses approximation ("~80%") rather than claiming exact figures
- Specifically mentions both "epochs and images" to be precise about what's being reduced
- Makes no claims beyond the observed data

3. Review Comments:
- The reviewer comments don't challenge this specific claim
- Methodological concerns raised in the review are about other aspects of the paper

The claim accurately reflects the evidence without exaggeration, making it well-stated (score of 1).