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:

starting with an existing human-like model, which we call the _student_, we fine-tune the model using a customized training curriculum.

The evidence to be evaluated is:In this paper, we choose to handle this trade-off by extending the Curriculum Learning concept of Bengio et al. (2009) to support varying both the curriculum and the labeling strategy (teacher). When training an existing human-like model on a particular curriculum and teacher, we adopt the fine-tuning methodology of McIlroy-Young et al. (2022b), which itself draws on recent methods from the transfer learning literature (Sun et al., 2019; Yosinski et al., 2014; Kornblith et al., 2019)

The goal of this work is to investigate data-efficient approaches for improving the strength of a given human-like model, while maintaining its human-like property.

Finally, we introduce our curriculum training approach, which extends the concept of curriculum learning to multiple teachers, enabling us to examine the impact of varying both the curriculum (dataset) and the teaching advice (label strategy). To develop human-like models, we replicate and extend the Maia framework developed by McIlroy-Young et al. (2020). We acquire a database of human chess moves from the online platform Lichess.org, where each move is labeled according to the player's Elo (skill) rating. We then group the moves by Elo rating, and use supervised learning to train a neural network to predict what move a human would play in a given chess position. For example, we train a model on moves made by players rated between 1150 to 1249, and we denote this model as Maia-1200.

Given a human-like model, we aim to improve its strength in a data-efficient manner, while maintaining its human similarity.

We formally introduce the notion of _curriculum training_ as the data-efficient fine-tuning of an existing human-like model, with the dual objectives of improving the model's strength and preserving the human similarity of the original model. In curriculum training, we start with a human-like model \(M\), which we call the _student_. Our goal is to choose a pair of _curriculum_\(X\) and _teacher_\(Y\) to update the given model.

After specifying the curriculum and teacher \((X,Y)\), we draw data points \(\{(x,y)\}\) from the provided distribution and use them to update the model \(M\). Following the curriculum learning approach, we update the model by performing gradient descent using this data. Note that our curriculum training notion extends the standard curriculum learning notion developed by Bengio et al. (2009) to settings with multiple teachers (labeling strategies).

We pre-trained a set of Maia models to serve as the initial human-like model \(M\) and the potential teachers \(Y\). We vary the choice of teachers \(Y\) to be different ratings of human players or human-like models, or a superhuman chess engine, and examine their impact. We also examine the impact of choosing curriculum \(X\) based on the potential mistakes that the student might make, using the assistance of a superhuman chess engine, stronger-rated Maia models, or a human expert.

We adopt the Maia-1200 model as our student (i.e., the initial human-like model we wish to improve), and update it using different designs of curriculum \(X\) and teacher \(Y\), varying both \(X\) and \(Y\) (SS4.1), varying only \(Y\) (SS4.2), and varying only \(X\) (SS4.3).

We first examine the most natural design of the curriculum and teacher pair: updating the initial human-like model Maia-1200 using data from higher-rated human players.

In this experiment, we set our student \(M\) as Maia-1200, and we vary the rating \(R\) from \(1400\) to \(3000\) and use \((X_{R},Y_{R})\) as the (curriculum, teacher) to update the student.

Due to a lack of data for higher-rated players, the size of the training curriculum is smaller for \(R=2600,2800\), and \(3000\) (with \(7300\), \(900\), and \(100\) batches, respectively).

We compute these evaluations every 10 batches between batch-0 and batch-100, and every 100 batches between batch-100 and batch-10,000. We repeat each set of evaluations 10 times, for a total of 10,240 games and 100 batches per evaluation, and report the moving average (with a window size of 50) of these measurements.

The results, as displayed in Figure 2, demonstrate the trade-offs of our three performance metrics for different pairs of curriculum and teacher \((X_{R},Y_{R})\). First of all, if our goal is purely to increase strength without concerns for human similarity or data efficiency, training using the highest rated curriculum leads to the strongest model. If our objective is to increase the model's strength while maintaining the model's human similarity above a selected threshold (without concern for data efficiency), using the closest-rated data for fine-tuning leads to the optimal outcome. For instance, if we want to maintain a move-matching accuracy of at least 49%, the optimal strategy is to train on 1400-rated data curriculum for 10,000 batches, which gives us a model with a 55% win probability.

If our objective is to maximize strength gains with a constraint on data usage, we see an interesting and non-trivial result: at many points during fine-tuning, the optimal curriculum is not the highest-rated or lowest-rated one, but at a point in between. For example, at batch-30, the 2000-rated curriculum is optimal, while at batch-100 and batch-300, the 2400-rated curriculum is optimal. Past this point, it appears that the 2600-rated curriculum is optimal at all batch sizes.

Overall, these results suggest that the strength of a teacher matters significantly when training models.

As an approximate solution, we use Maia models that we have trained to serve as the teacher \(Y\). Thus, we teach Maia-1200 using a curriculum where \(X\) is the set of positions encountered by 1200-rated humans, and \(Y\) are the moves that Maia-1400 would make in these positions.

Instead, we use specific domain knowledge in chess to develop our own sets of curricula based on fixing mistakes made by the student. We list three categories of mistakes, and choose one representative curriculum for each category to analyze.

As this is also a common problem for human chess players, we selected this as our representative curriculum.

* Mistakes identified by a _higher-rated human-like model_.

We selected positions where Maia-1200 and Maia-1400 differ, which we denote as 1400-inflection positions. Using these curricula and the Maia-2400 teacher, we ran another experiment with the same training procedure and evaluation methods as Experiments 1 and 2.

If our objective is to maximize strength gains with a constraint on the data usage, both the blunder curriculum and the 1400-inflection curriculum are more efficient at teaching the base model than a random curriculum, while the rooks curriculum underperforms in this regard.

On the other hand, the blunder and 1400-inflection curricula (positions identified by superhuman and human-like AIs) look at mistakes that the model makes across the entire distribution of general board states, but the emphasis on mistakes leads to better outcomes than no emphasis. If our objective is to increase the model strength while ensuring the model maintains human similarity above a selected threshold, the blunder curriculum outperforms random while the 1400-inflection and rooks curriculum do not.

After a model undergoes curriculum training, the model will naturally degrade in its initial performance, because we are teaching it to play differently. However, the updated model might in fact play more like a higher-rated model because we have taught it to play better. To investigate this, we took the 11 models trained in Experiments 2 and 3 after learning the full 10,000-batch curriculum, and analyzed their move-matching accuracy against higher-rated datasets, to see if the updated models play like higher-rated humans.

Overall, we find that the updated models are more similar to higher-rated datasets than the original dataset. Looking at the models from Experiment 2, where we varied the model's teacher, there is a very strong correlation between the rating of the teacher and the most similar dataset of the updated model. For instance, the model taught by Maia-1600 is most similar to the 1600-rated dataset, while the model taught by Maia-2400 is most similar to the 2400-rated dataset.

In this paper, we propose a generalized framework for improving a human-like model while maintaining its human similarity, using a novel extension of curriculum learning which considers both the curriculum data to train on, and the teacher used to label this data.

Nevertheless, the results in SS4.4 suggest that our curriculum training methodology is able to improve human-like models in a way that ensures human similarity at their improved level of play, on a path similar to human learning.

This work develops a methodology for improving the strength of a given human-like model, while maintaining its human-like property. The human-like models we create are trained on aggregate human data grouped by coarse skill levels, similar to the models trained by McIlroy-Young et al. (2020) (though our strongest human-like models surpass theirs in target Elo rating).

In this work, we train human-like models using a publicly available dataset from Lichess.org.

Details on how the models were trained is available in the Appendix.

Input: Figure 2: The results of using higher-rated player data to fine-tune a lower-rated student model. Each curve represents the change in human similarity and strength when fine-tuning the Maia-1200 model on increasing amounts of data from players of a particular rating. For each curve, we label the points that are trained using different numbers of batches, from 10 batches to 100,000 batches.

Input: Figure 5: The results of analyzing how similar curriculum-updated models are to human datasets at a range of skill levels.

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, the claim "starting with an existing human-like model, which we call the student, we fine-tune the model using a customized training curriculum" appears to be well-stated and accurately reflects the methodology described in the evidence. The paper thoroughly details how they start with the Maia-1200 model as the student and employ various curricula and teaching strategies to fine-tune it. The evidence includes comprehensive experimental details, clear methodology, and multiple evaluations that directly support this claim. The authors do not overstate their approach and provide substantial data to back up their training process.

<score>1</score>

Justification:
1. The evidence directly supports each component of the claim:
- "Existing human-like model": Uses pre-trained Maia models, specifically Maia-1200 as the student
- "Fine-tuning": Details the fine-tuning methodology based on McIlroy-Young et al.
- "Customized training curriculum": Extensively documents different curriculum approaches including varying ratings, mistake-based curricula, and different teaching strategies

2. The evidence provides:
- Clear experimental methodology
- Detailed evaluation metrics
- Visual results in Figures 2 and 5 showing the effects of different curricula
- Transparent discussion of limitations like data availability for higher ratings

3. No exaggeration or overstatement is present in the claim - it simply states what was actually done, supported by extensive evidence.


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:

starting with an existing human-like model, which we call the _student_, we fine-tune the model using a customized training curriculum.

The review comment to be evaluated is:

The authors offer a quite extensive evaluation of the proposed approach as well as the basic models used. This evaluation includes a consideration of differences between the used positions and the source (teacher) of the proposed move. Therefore, the evaluation is beyond what is usually expected. However, the used definition of the "human similarity" metric is problematic. For one, expecting an exact matching of ground truth moves is very strict and a similarity metric should be preferred. Although, this is likely not a trivial task, because similarity to human play can potentially be only evaluated by human experts. However, the phrase "behavioral data used to train the original model" suggested, that in most experiments, only moves from the Maia-1200 set are considered. (Also see clarity) In case this understanding is correct, the metric only evaluates deviation from novice play - not from human play. Therefore, the correlation between increase in playing strength and decrease of similarity becomes obvious. A potential resolution could be comparing to any human play (independent of the playing strength or up to the teachers playing strength). This should be an approximate if any human would play like the engine.
Another problem is, that it seems, that learning approach has not converged, after using all batches once (Fig 2,3). Without convergence, the used learning rate becomes an interesting question, because it implicitly defines the trade-off between initial weights and new data.
Furthermore, Section 3.3. is a bit lacking - considering it is the introduction of the main approach. (See clarity)

The evidence to be evaluated is:In this paper, we choose to handle this trade-off by extending the Curriculum Learning concept of Bengio et al. (2009) to support varying both the curriculum and the labeling strategy (teacher). When training an existing human-like model on a particular curriculum and teacher, we adopt the fine-tuning methodology of McIlroy-Young et al. (2022b), which itself draws on recent methods from the transfer learning literature (Sun et al., 2019; Yosinski et al., 2014; Kornblith et al., 2019)

The goal of this work is to investigate data-efficient approaches for improving the strength of a given human-like model, while maintaining its human-like property.

Finally, we introduce our curriculum training approach, which extends the concept of curriculum learning to multiple teachers, enabling us to examine the impact of varying both the curriculum (dataset) and the teaching advice (label strategy). To develop human-like models, we replicate and extend the Maia framework developed by McIlroy-Young et al. (2020). We acquire a database of human chess moves from the online platform Lichess.org, where each move is labeled according to the player's Elo (skill) rating. We then group the moves by Elo rating, and use supervised learning to train a neural network to predict what move a human would play in a given chess position. For example, we train a model on moves made by players rated between 1150 to 1249, and we denote this model as Maia-1200.

Given a human-like model, we aim to improve its strength in a data-efficient manner, while maintaining its human similarity.

We formally introduce the notion of _curriculum training_ as the data-efficient fine-tuning of an existing human-like model, with the dual objectives of improving the model's strength and preserving the human similarity of the original model. In curriculum training, we start with a human-like model \(M\), which we call the _student_. Our goal is to choose a pair of _curriculum_\(X\) and _teacher_\(Y\) to update the given model.

After specifying the curriculum and teacher \((X,Y)\), we draw data points \(\{(x,y)\}\) from the provided distribution and use them to update the model \(M\). Following the curriculum learning approach, we update the model by performing gradient descent using this data. Note that our curriculum training notion extends the standard curriculum learning notion developed by Bengio et al. (2009) to settings with multiple teachers (labeling strategies).

We pre-trained a set of Maia models to serve as the initial human-like model \(M\) and the potential teachers \(Y\). We vary the choice of teachers \(Y\) to be different ratings of human players or human-like models, or a superhuman chess engine, and examine their impact. We also examine the impact of choosing curriculum \(X\) based on the potential mistakes that the student might make, using the assistance of a superhuman chess engine, stronger-rated Maia models, or a human expert.

We adopt the Maia-1200 model as our student (i.e., the initial human-like model we wish to improve), and update it using different designs of curriculum \(X\) and teacher \(Y\), varying both \(X\) and \(Y\) (SS4.1), varying only \(Y\) (SS4.2), and varying only \(X\) (SS4.3).

We first examine the most natural design of the curriculum and teacher pair: updating the initial human-like model Maia-1200 using data from higher-rated human players.

In this experiment, we set our student \(M\) as Maia-1200, and we vary the rating \(R\) from \(1400\) to \(3000\) and use \((X_{R},Y_{R})\) as the (curriculum, teacher) to update the student.

Due to a lack of data for higher-rated players, the size of the training curriculum is smaller for \(R=2600,2800\), and \(3000\) (with \(7300\), \(900\), and \(100\) batches, respectively).

We compute these evaluations every 10 batches between batch-0 and batch-100, and every 100 batches between batch-100 and batch-10,000. We repeat each set of evaluations 10 times, for a total of 10,240 games and 100 batches per evaluation, and report the moving average (with a window size of 50) of these measurements.

The results, as displayed in Figure 2, demonstrate the trade-offs of our three performance metrics for different pairs of curriculum and teacher \((X_{R},Y_{R})\). First of all, if our goal is purely to increase strength without concerns for human similarity or data efficiency, training using the highest rated curriculum leads to the strongest model. If our objective is to increase the model's strength while maintaining the model's human similarity above a selected threshold (without concern for data efficiency), using the closest-rated data for fine-tuning leads to the optimal outcome. For instance, if we want to maintain a move-matching accuracy of at least 49%, the optimal strategy is to train on 1400-rated data curriculum for 10,000 batches, which gives us a model with a 55% win probability.

If our objective is to maximize strength gains with a constraint on data usage, we see an interesting and non-trivial result: at many points during fine-tuning, the optimal curriculum is not the highest-rated or lowest-rated one, but at a point in between. For example, at batch-30, the 2000-rated curriculum is optimal, while at batch-100 and batch-300, the 2400-rated curriculum is optimal. Past this point, it appears that the 2600-rated curriculum is optimal at all batch sizes.

Overall, these results suggest that the strength of a teacher matters significantly when training models.

As an approximate solution, we use Maia models that we have trained to serve as the teacher \(Y\). Thus, we teach Maia-1200 using a curriculum where \(X\) is the set of positions encountered by 1200-rated humans, and \(Y\) are the moves that Maia-1400 would make in these positions.

Instead, we use specific domain knowledge in chess to develop our own sets of curricula based on fixing mistakes made by the student. We list three categories of mistakes, and choose one representative curriculum for each category to analyze.

As this is also a common problem for human chess players, we selected this as our representative curriculum.

* Mistakes identified by a _higher-rated human-like model_.

We selected positions where Maia-1200 and Maia-1400 differ, which we denote as 1400-inflection positions. Using these curricula and the Maia-2400 teacher, we ran another experiment with the same training procedure and evaluation methods as Experiments 1 and 2.

If our objective is to maximize strength gains with a constraint on the data usage, both the blunder curriculum and the 1400-inflection curriculum are more efficient at teaching the base model than a random curriculum, while the rooks curriculum underperforms in this regard.

On the other hand, the blunder and 1400-inflection curricula (positions identified by superhuman and human-like AIs) look at mistakes that the model makes across the entire distribution of general board states, but the emphasis on mistakes leads to better outcomes than no emphasis. If our objective is to increase the model strength while ensuring the model maintains human similarity above a selected threshold, the blunder curriculum outperforms random while the 1400-inflection and rooks curriculum do not.

After a model undergoes curriculum training, the model will naturally degrade in its initial performance, because we are teaching it to play differently. However, the updated model might in fact play more like a higher-rated model because we have taught it to play better. To investigate this, we took the 11 models trained in Experiments 2 and 3 after learning the full 10,000-batch curriculum, and analyzed their move-matching accuracy against higher-rated datasets, to see if the updated models play like higher-rated humans.

Overall, we find that the updated models are more similar to higher-rated datasets than the original dataset. Looking at the models from Experiment 2, where we varied the model's teacher, there is a very strong correlation between the rating of the teacher and the most similar dataset of the updated model. For instance, the model taught by Maia-1600 is most similar to the 1600-rated dataset, while the model taught by Maia-2400 is most similar to the 2400-rated dataset.

In this paper, we propose a generalized framework for improving a human-like model while maintaining its human similarity, using a novel extension of curriculum learning which considers both the curriculum data to train on, and the teacher used to label this data.

Nevertheless, the results in SS4.4 suggest that our curriculum training methodology is able to improve human-like models in a way that ensures human similarity at their improved level of play, on a path similar to human learning.

This work develops a methodology for improving the strength of a given human-like model, while maintaining its human-like property. The human-like models we create are trained on aggregate human data grouped by coarse skill levels, similar to the models trained by McIlroy-Young et al. (2020) (though our strongest human-like models surpass theirs in target Elo rating).

In this work, we train human-like models using a publicly available dataset from Lichess.org.

Details on how the models were trained is available in the Appendix.

Input: Figure 2: The results of using higher-rated player data to fine-tune a lower-rated student model. Each curve represents the change in human similarity and strength when fine-tuning the Maia-1200 model on increasing amounts of data from players of a particular rating. For each curve, we label the points that are trained using different numbers of batches, from 10 batches to 100,000 batches.

Input: Figure 5: The results of analyzing how similar curriculum-updated models are to human datasets at a range of skill levels.

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, the claim "starting with an existing human-like model, which we call the student, we fine-tune the model using a customized training curriculum" appears to be well-stated and accurately reflects the methodology presented in the paper. The evidence thoroughly documents their approach of using Maia-1200 as the student model and applying curriculum learning with various teachers and datasets. The paper provides extensive experimental details, figures showing the training progression, and clear explanations of the methodology. The claim avoids exaggeration and is directly supported by the detailed evidence presented.

<score>1</score>

Justification:
- The paper clearly demonstrates the use of an existing model (Maia-1200) as the student
- Provides detailed evidence of the curriculum training methodology through multiple experiments
- Includes comprehensive figures (Fig 2, 5) showing the training process and results
- The review comment, while raising some concerns about metrics and convergence, does not dispute the basic training approach described in the claim
- The wording is appropriately measured and matches the evidence without overstatement


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:

starting with an existing human-like model, which we call the _student_, we fine-tune the model using a customized training curriculum.

The review comment to be evaluated is:

## Strengths
The writing of the paper is exemplary, making it relatively easy to understand. I particularly liked the sec. 1 motivation behind the need for human-like models, even though they may have a worse performance than the models not taking this into account.

## Weaknesses
I find the evaluation of the human likeness of the models to be performed on the original datasets (of the original training, not the finetuning) unacceptable. Doing this makes the metric to count both "unhuman" and "human, but of higher-rating" as unhuman behavior, which doesn't follow the motivation of improving the models while keeping them human. While the sec. 4.4 shows partial results on the target rating, I find presentation of the original rating results as confusing, and the sec. 4.4 to not be detailed enough to override this assessment.

If the proper train/test split was not used, the results may suffer from overfitting (ie. the reported human likeness would not constitute a good approximation of a real move-matching accuracy on new positions).

The presented results provide no new insights for me, and are predictable:

1. As human likeness measures accuracy of reproducing the original data, it drops when finetuning on different data [4]. The higher the rating gap (=> bigger domain shift), the bigger the catastrophic forgetting.
2. As we train on the data with humans of a better rating, the performance (assuming enough data) improves, as the model gets to reproduce the better moves.
3. The initial speed of improvement is lower for datasets that are more different (ie. higher rating) from the original, due to less common features being reusable in the new domain. This implies that, for a fixed sample budget, to get the best final performance there will be a tradeoff between low rating (=> the domain is close and there are a lot of reusable features), and high rating (=> overall better moves to reproduce). Authors also provide a correct intuition in the paper behind this: "After a model undergoes curriculum training, [it] will naturally degrade in its initial performance, because we are teaching it to play differently".

I don't find the results in Experiment 3 very informative, as it's hard to understand the difference between the handcrafted data distributions (is the score lower because the data is of less variance, or coming from different-rating source, or the chosen positions don't cover the space of positions well enough to improve the model, etc.).

The benefits of introducing the curriculum training framework are not clear to me: the concrete method for updating the network (finetuning) is prior work, as is generating the actions from previous trajectories or other models. The paper makes no claims about any of the instantiation of the framework (using trajectories or other models' actions, or changing the states only) as being empirically better then the others, nor does it compare to a natural baseline of training on the new dataset from scratch.

## Actionable feedback

1. Present the results in the experimental section on the target domain (ie. it's human if it's close to what the human of a given strength would play)
2. Clarify whether a train/test split was used in the paper and if not, provide the results where the evaluation data is separate from the training one.
3. Provide a clearer value for an ICLR reader: what it is actually supposed to learn from the paper? The reader would be aware of the techniques for improving the models from the literature, is one of the methods better than the others, in what situations? Which of the claims are only applicable in the domain of human-like models or domain of chess? Is there anything else to take home?

[4]: https://en.wikipedia.org/wiki/Catastrophic_interference

The evidence to be evaluated is:In this paper, we choose to handle this trade-off by extending the Curriculum Learning concept of Bengio et al. (2009) to support varying both the curriculum and the labeling strategy (teacher). When training an existing human-like model on a particular curriculum and teacher, we adopt the fine-tuning methodology of McIlroy-Young et al. (2022b), which itself draws on recent methods from the transfer learning literature (Sun et al., 2019; Yosinski et al., 2014; Kornblith et al., 2019)

The goal of this work is to investigate data-efficient approaches for improving the strength of a given human-like model, while maintaining its human-like property.

Finally, we introduce our curriculum training approach, which extends the concept of curriculum learning to multiple teachers, enabling us to examine the impact of varying both the curriculum (dataset) and the teaching advice (label strategy). To develop human-like models, we replicate and extend the Maia framework developed by McIlroy-Young et al. (2020). We acquire a database of human chess moves from the online platform Lichess.org, where each move is labeled according to the player's Elo (skill) rating. We then group the moves by Elo rating, and use supervised learning to train a neural network to predict what move a human would play in a given chess position. For example, we train a model on moves made by players rated between 1150 to 1249, and we denote this model as Maia-1200.

Given a human-like model, we aim to improve its strength in a data-efficient manner, while maintaining its human similarity.

We formally introduce the notion of _curriculum training_ as the data-efficient fine-tuning of an existing human-like model, with the dual objectives of improving the model's strength and preserving the human similarity of the original model. In curriculum training, we start with a human-like model \(M\), which we call the _student_. Our goal is to choose a pair of _curriculum_\(X\) and _teacher_\(Y\) to update the given model.

After specifying the curriculum and teacher \((X,Y)\), we draw data points \(\{(x,y)\}\) from the provided distribution and use them to update the model \(M\). Following the curriculum learning approach, we update the model by performing gradient descent using this data. Note that our curriculum training notion extends the standard curriculum learning notion developed by Bengio et al. (2009) to settings with multiple teachers (labeling strategies).

We pre-trained a set of Maia models to serve as the initial human-like model \(M\) and the potential teachers \(Y\). We vary the choice of teachers \(Y\) to be different ratings of human players or human-like models, or a superhuman chess engine, and examine their impact. We also examine the impact of choosing curriculum \(X\) based on the potential mistakes that the student might make, using the assistance of a superhuman chess engine, stronger-rated Maia models, or a human expert.

We adopt the Maia-1200 model as our student (i.e., the initial human-like model we wish to improve), and update it using different designs of curriculum \(X\) and teacher \(Y\), varying both \(X\) and \(Y\) (SS4.1), varying only \(Y\) (SS4.2), and varying only \(X\) (SS4.3).

We first examine the most natural design of the curriculum and teacher pair: updating the initial human-like model Maia-1200 using data from higher-rated human players.

In this experiment, we set our student \(M\) as Maia-1200, and we vary the rating \(R\) from \(1400\) to \(3000\) and use \((X_{R},Y_{R})\) as the (curriculum, teacher) to update the student.

Due to a lack of data for higher-rated players, the size of the training curriculum is smaller for \(R=2600,2800\), and \(3000\) (with \(7300\), \(900\), and \(100\) batches, respectively).

We compute these evaluations every 10 batches between batch-0 and batch-100, and every 100 batches between batch-100 and batch-10,000. We repeat each set of evaluations 10 times, for a total of 10,240 games and 100 batches per evaluation, and report the moving average (with a window size of 50) of these measurements.

The results, as displayed in Figure 2, demonstrate the trade-offs of our three performance metrics for different pairs of curriculum and teacher \((X_{R},Y_{R})\). First of all, if our goal is purely to increase strength without concerns for human similarity or data efficiency, training using the highest rated curriculum leads to the strongest model. If our objective is to increase the model's strength while maintaining the model's human similarity above a selected threshold (without concern for data efficiency), using the closest-rated data for fine-tuning leads to the optimal outcome. For instance, if we want to maintain a move-matching accuracy of at least 49%, the optimal strategy is to train on 1400-rated data curriculum for 10,000 batches, which gives us a model with a 55% win probability.

If our objective is to maximize strength gains with a constraint on data usage, we see an interesting and non-trivial result: at many points during fine-tuning, the optimal curriculum is not the highest-rated or lowest-rated one, but at a point in between. For example, at batch-30, the 2000-rated curriculum is optimal, while at batch-100 and batch-300, the 2400-rated curriculum is optimal. Past this point, it appears that the 2600-rated curriculum is optimal at all batch sizes.

Overall, these results suggest that the strength of a teacher matters significantly when training models.

As an approximate solution, we use Maia models that we have trained to serve as the teacher \(Y\). Thus, we teach Maia-1200 using a curriculum where \(X\) is the set of positions encountered by 1200-rated humans, and \(Y\) are the moves that Maia-1400 would make in these positions.

Instead, we use specific domain knowledge in chess to develop our own sets of curricula based on fixing mistakes made by the student. We list three categories of mistakes, and choose one representative curriculum for each category to analyze.

As this is also a common problem for human chess players, we selected this as our representative curriculum.

* Mistakes identified by a _higher-rated human-like model_.

We selected positions where Maia-1200 and Maia-1400 differ, which we denote as 1400-inflection positions. Using these curricula and the Maia-2400 teacher, we ran another experiment with the same training procedure and evaluation methods as Experiments 1 and 2.

If our objective is to maximize strength gains with a constraint on the data usage, both the blunder curriculum and the 1400-inflection curriculum are more efficient at teaching the base model than a random curriculum, while the rooks curriculum underperforms in this regard.

On the other hand, the blunder and 1400-inflection curricula (positions identified by superhuman and human-like AIs) look at mistakes that the model makes across the entire distribution of general board states, but the emphasis on mistakes leads to better outcomes than no emphasis. If our objective is to increase the model strength while ensuring the model maintains human similarity above a selected threshold, the blunder curriculum outperforms random while the 1400-inflection and rooks curriculum do not.

After a model undergoes curriculum training, the model will naturally degrade in its initial performance, because we are teaching it to play differently. However, the updated model might in fact play more like a higher-rated model because we have taught it to play better. To investigate this, we took the 11 models trained in Experiments 2 and 3 after learning the full 10,000-batch curriculum, and analyzed their move-matching accuracy against higher-rated datasets, to see if the updated models play like higher-rated humans.

Overall, we find that the updated models are more similar to higher-rated datasets than the original dataset. Looking at the models from Experiment 2, where we varied the model's teacher, there is a very strong correlation between the rating of the teacher and the most similar dataset of the updated model. For instance, the model taught by Maia-1600 is most similar to the 1600-rated dataset, while the model taught by Maia-2400 is most similar to the 2400-rated dataset.

In this paper, we propose a generalized framework for improving a human-like model while maintaining its human similarity, using a novel extension of curriculum learning which considers both the curriculum data to train on, and the teacher used to label this data.

Nevertheless, the results in SS4.4 suggest that our curriculum training methodology is able to improve human-like models in a way that ensures human similarity at their improved level of play, on a path similar to human learning.

This work develops a methodology for improving the strength of a given human-like model, while maintaining its human-like property. The human-like models we create are trained on aggregate human data grouped by coarse skill levels, similar to the models trained by McIlroy-Young et al. (2020) (though our strongest human-like models surpass theirs in target Elo rating).

In this work, we train human-like models using a publicly available dataset from Lichess.org.

Details on how the models were trained is available in the Appendix.

Input: Figure 2: The results of using higher-rated player data to fine-tune a lower-rated student model. Each curve represents the change in human similarity and strength when fine-tuning the Maia-1200 model on increasing amounts of data from players of a particular rating. For each curve, we label the points that are trained using different numbers of batches, from 10 batches to 100,000 batches.

Input: Figure 5: The results of analyzing how similar curriculum-updated models are to human datasets at a range of skill levels.

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, the statement appears to be well-stated. The evidence clearly demonstrates that they do start with an existing human-like model (specifically Maia-1200 as the "student") and fine-tune it using a customized training curriculum. This is supported by detailed methodology descriptions, experimental results shown in Figures 2 and 5, and clear explanations of how they vary both the curriculum (X) and teacher (Y) during training. The reviewer's comments, while critical of some aspects, don't dispute this basic claim. The claim is straightforward and accurately reflects what was done without exaggeration.

<score>1</score>

Justification:
1. Direct evidence: "We adopt the Maia-1200 model as our student (i.e., the initial human-like model we wish to improve), and update it using different designs of curriculum X and teacher Y"
2. Methodology is clearly documented with specific examples of curriculum choices
3. Results in Figures 2 and 5 demonstrate the implementation of this approach
4. The claim is basic and descriptive, avoiding any unsupported assertions about effectiveness or superiority
5. Even the critical reviewer comments don't challenge this foundational aspect of the work


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:

starting with an existing human-like model, which we call the _student_, we fine-tune the model using a customized training curriculum.

The review comment to be evaluated is:

Strength:
This paper studies curriculum learning and human-like models in the widely-studied domain of Chess. They provide detailed experiments to find the relation between strength and similarity.

Weaknesses:
However, there are no new techniques in the methodology, and the experiment results are not surprising. To be more specific, they used transferring learning to fine-tune a 1200m model with datasets of different ratings. It is very obvious that the playing strengths will increase and the human similarity will decrease when comparing the fine-tuned model with the original one. Besides, in subsection 4-4, the author said “the model taught by Maia-1600 is most similar to the 1600-rated dataset, while the model taught by Maia-2400 is most similar to the 2400-rated dataset”. This conclusion does not give any new findings. Overall, there are no new findings and the novelty is poor.

Other comments:
* Fig. 1 and Fig. 5 only provide the decimal numbers, while the readers might be interested in the actual difference in smaller grains.
* In experiment 4.1, the author said “at least 49% … the optimal strategy is to train on 1400-rated data curriculum for 10,000 batches … with a 55% win probability”, and “a 55% win probability by training on a 1800-rated curriculum for 1000 batches but at the cost of a much lower move-matching accuracy.” But according to Fig. 2, the move-matching accuracy is about 48.6% for 1800-rated curriculum of 1000 batches, which is not “much lower” as stated in the context. The results seem to have not much difference when comparing different settings.
* The author should explain how a move is generated by the trained model, is it by MCTS or just by the move with the maximum policy?
* The author claims to propose a framework for curriculum learning, but according to the contents of the paper, this seems exaggerated.
* In subsection 4.3, the author should provide the total number of positions for the three categories of mistakes. Especially for human experts, the 80 simulated games seem to be much smaller than the other two categories which can be generated by programs.
* In Fig. 1 and Fig. 5, what does the color mean?
* On page 5, “50% .” -> “50%.”
* In Data Pipeline section of the appendix, “from 2021-06 to 2021-03”, is it typo for “2021-06 to 2022-03”?

The evidence to be evaluated is:In this paper, we choose to handle this trade-off by extending the Curriculum Learning concept of Bengio et al. (2009) to support varying both the curriculum and the labeling strategy (teacher). When training an existing human-like model on a particular curriculum and teacher, we adopt the fine-tuning methodology of McIlroy-Young et al. (2022b), which itself draws on recent methods from the transfer learning literature (Sun et al., 2019; Yosinski et al., 2014; Kornblith et al., 2019)

The goal of this work is to investigate data-efficient approaches for improving the strength of a given human-like model, while maintaining its human-like property.

Finally, we introduce our curriculum training approach, which extends the concept of curriculum learning to multiple teachers, enabling us to examine the impact of varying both the curriculum (dataset) and the teaching advice (label strategy). To develop human-like models, we replicate and extend the Maia framework developed by McIlroy-Young et al. (2020). We acquire a database of human chess moves from the online platform Lichess.org, where each move is labeled according to the player's Elo (skill) rating. We then group the moves by Elo rating, and use supervised learning to train a neural network to predict what move a human would play in a given chess position. For example, we train a model on moves made by players rated between 1150 to 1249, and we denote this model as Maia-1200.

Given a human-like model, we aim to improve its strength in a data-efficient manner, while maintaining its human similarity.

We formally introduce the notion of _curriculum training_ as the data-efficient fine-tuning of an existing human-like model, with the dual objectives of improving the model's strength and preserving the human similarity of the original model. In curriculum training, we start with a human-like model \(M\), which we call the _student_. Our goal is to choose a pair of _curriculum_\(X\) and _teacher_\(Y\) to update the given model.

After specifying the curriculum and teacher \((X,Y)\), we draw data points \(\{(x,y)\}\) from the provided distribution and use them to update the model \(M\). Following the curriculum learning approach, we update the model by performing gradient descent using this data. Note that our curriculum training notion extends the standard curriculum learning notion developed by Bengio et al. (2009) to settings with multiple teachers (labeling strategies).

We pre-trained a set of Maia models to serve as the initial human-like model \(M\) and the potential teachers \(Y\). We vary the choice of teachers \(Y\) to be different ratings of human players or human-like models, or a superhuman chess engine, and examine their impact. We also examine the impact of choosing curriculum \(X\) based on the potential mistakes that the student might make, using the assistance of a superhuman chess engine, stronger-rated Maia models, or a human expert.

We adopt the Maia-1200 model as our student (i.e., the initial human-like model we wish to improve), and update it using different designs of curriculum \(X\) and teacher \(Y\), varying both \(X\) and \(Y\) (SS4.1), varying only \(Y\) (SS4.2), and varying only \(X\) (SS4.3).

We first examine the most natural design of the curriculum and teacher pair: updating the initial human-like model Maia-1200 using data from higher-rated human players.

In this experiment, we set our student \(M\) as Maia-1200, and we vary the rating \(R\) from \(1400\) to \(3000\) and use \((X_{R},Y_{R})\) as the (curriculum, teacher) to update the student.

Due to a lack of data for higher-rated players, the size of the training curriculum is smaller for \(R=2600,2800\), and \(3000\) (with \(7300\), \(900\), and \(100\) batches, respectively).

We compute these evaluations every 10 batches between batch-0 and batch-100, and every 100 batches between batch-100 and batch-10,000. We repeat each set of evaluations 10 times, for a total of 10,240 games and 100 batches per evaluation, and report the moving average (with a window size of 50) of these measurements.

The results, as displayed in Figure 2, demonstrate the trade-offs of our three performance metrics for different pairs of curriculum and teacher \((X_{R},Y_{R})\). First of all, if our goal is purely to increase strength without concerns for human similarity or data efficiency, training using the highest rated curriculum leads to the strongest model. If our objective is to increase the model's strength while maintaining the model's human similarity above a selected threshold (without concern for data efficiency), using the closest-rated data for fine-tuning leads to the optimal outcome. For instance, if we want to maintain a move-matching accuracy of at least 49%, the optimal strategy is to train on 1400-rated data curriculum for 10,000 batches, which gives us a model with a 55% win probability.

If our objective is to maximize strength gains with a constraint on data usage, we see an interesting and non-trivial result: at many points during fine-tuning, the optimal curriculum is not the highest-rated or lowest-rated one, but at a point in between. For example, at batch-30, the 2000-rated curriculum is optimal, while at batch-100 and batch-300, the 2400-rated curriculum is optimal. Past this point, it appears that the 2600-rated curriculum is optimal at all batch sizes.

Overall, these results suggest that the strength of a teacher matters significantly when training models.

As an approximate solution, we use Maia models that we have trained to serve as the teacher \(Y\). Thus, we teach Maia-1200 using a curriculum where \(X\) is the set of positions encountered by 1200-rated humans, and \(Y\) are the moves that Maia-1400 would make in these positions.

Instead, we use specific domain knowledge in chess to develop our own sets of curricula based on fixing mistakes made by the student. We list three categories of mistakes, and choose one representative curriculum for each category to analyze.

As this is also a common problem for human chess players, we selected this as our representative curriculum.

* Mistakes identified by a _higher-rated human-like model_.

We selected positions where Maia-1200 and Maia-1400 differ, which we denote as 1400-inflection positions. Using these curricula and the Maia-2400 teacher, we ran another experiment with the same training procedure and evaluation methods as Experiments 1 and 2.

If our objective is to maximize strength gains with a constraint on the data usage, both the blunder curriculum and the 1400-inflection curriculum are more efficient at teaching the base model than a random curriculum, while the rooks curriculum underperforms in this regard.

On the other hand, the blunder and 1400-inflection curricula (positions identified by superhuman and human-like AIs) look at mistakes that the model makes across the entire distribution of general board states, but the emphasis on mistakes leads to better outcomes than no emphasis. If our objective is to increase the model strength while ensuring the model maintains human similarity above a selected threshold, the blunder curriculum outperforms random while the 1400-inflection and rooks curriculum do not.

After a model undergoes curriculum training, the model will naturally degrade in its initial performance, because we are teaching it to play differently. However, the updated model might in fact play more like a higher-rated model because we have taught it to play better. To investigate this, we took the 11 models trained in Experiments 2 and 3 after learning the full 10,000-batch curriculum, and analyzed their move-matching accuracy against higher-rated datasets, to see if the updated models play like higher-rated humans.

Overall, we find that the updated models are more similar to higher-rated datasets than the original dataset. Looking at the models from Experiment 2, where we varied the model's teacher, there is a very strong correlation between the rating of the teacher and the most similar dataset of the updated model. For instance, the model taught by Maia-1600 is most similar to the 1600-rated dataset, while the model taught by Maia-2400 is most similar to the 2400-rated dataset.

In this paper, we propose a generalized framework for improving a human-like model while maintaining its human similarity, using a novel extension of curriculum learning which considers both the curriculum data to train on, and the teacher used to label this data.

Nevertheless, the results in SS4.4 suggest that our curriculum training methodology is able to improve human-like models in a way that ensures human similarity at their improved level of play, on a path similar to human learning.

This work develops a methodology for improving the strength of a given human-like model, while maintaining its human-like property. The human-like models we create are trained on aggregate human data grouped by coarse skill levels, similar to the models trained by McIlroy-Young et al. (2020) (though our strongest human-like models surpass theirs in target Elo rating).

In this work, we train human-like models using a publicly available dataset from Lichess.org.

Details on how the models were trained is available in the Appendix.

Input: Figure 2: The results of using higher-rated player data to fine-tune a lower-rated student model. Each curve represents the change in human similarity and strength when fine-tuning the Maia-1200 model on increasing amounts of data from players of a particular rating. For each curve, we label the points that are trained using different numbers of batches, from 10 batches to 100,000 batches.

Input: Figure 5: The results of analyzing how similar curriculum-updated models are to human datasets at a range of skill levels.

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 "starting with an existing human-like model, which we call the student, we fine-tune the model using a customized training curriculum," I find this to be a well-stated claim that accurately reflects the evidence without exaggeration.

The evidence clearly shows they:
1. Start with Maia-1200 as the student model
2. Use various customized curricula (higher-rated player data, specific mistake categories, etc.)
3. Implement fine-tuning methodology
4. Provide detailed experimental results showing the effects

The reviewer comments don't dispute this basic methodological claim, though they question the novelty of results. The claim simply states what they did without overstating the innovation or results.

<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:

starting with an existing human-like model, which we call the _student_, we fine-tune the model using a customized training curriculum.

The review comment to be evaluated is:

Strengths

- **************Clarity**************: The paper is well written and easy to understand, and the sections naturally follow from one to the other.
- **********Novelty:********** The paper is relatively novel in its approach towards understanding curriculum learning and building human-like models. While the zone of proximal development (ZPD: problems with medium difficulty are the most useful) have been studied by measuring problem difficulty, the paper looks at teacher/annotator proficiency.
- The analysis in the paper is thorough, and the graphs and methods are easy to understand.
- The insight of accounting for labeller/ teacher proficiency is quite powerful.

Weakness

- The interpretation of the result (most data-efficient curriculum is often not the highest or lowest-rated teacher, but somewhere in the middle) in Experiment 2 is not well supported. The authors themselves suggest the alternate hypothesis that seems more likely: Maia- 1200 cannot account for the stylistic differences between a human-like model and an alpha-beta model like Stockfish. The alternate hypothesis seems likelier as the most similar stylistic teachers with the highest ratings are the best teachers. Without an experiment to test this, one of the central claims of the paper is not on firm grounds.
- I am unsure how the results in this paper might extend to a domain outside of chess: either for teaching people or for building stronger human-like models. The discussion section is lacking here, with just one application domain in the paper! For example, the claim that “that the teacher is much more important than the curriculum.” might not extend to other domains.
- Experiments 2,3 and Figures 2 and 3 show human similarity based on the games of 1200 rated players. When improving the strength of a model, one might want to see a resemblance closer to the target rating rather than the base rating. So, if I wish to have a certain similarity with a 1600-rated human, would it be better to train using a 1600 agent or a 1400 agent or an 1800 agent? Using matching accuracy from 1200-rated games does not seem right (this is also seen in experiment 4).

Minor

- The caption or the graph in figure 5 does not mention the metric shown.

The evidence to be evaluated is:In this paper, we choose to handle this trade-off by extending the Curriculum Learning concept of Bengio et al. (2009) to support varying both the curriculum and the labeling strategy (teacher). When training an existing human-like model on a particular curriculum and teacher, we adopt the fine-tuning methodology of McIlroy-Young et al. (2022b), which itself draws on recent methods from the transfer learning literature (Sun et al., 2019; Yosinski et al., 2014; Kornblith et al., 2019)

The goal of this work is to investigate data-efficient approaches for improving the strength of a given human-like model, while maintaining its human-like property.

Finally, we introduce our curriculum training approach, which extends the concept of curriculum learning to multiple teachers, enabling us to examine the impact of varying both the curriculum (dataset) and the teaching advice (label strategy). To develop human-like models, we replicate and extend the Maia framework developed by McIlroy-Young et al. (2020). We acquire a database of human chess moves from the online platform Lichess.org, where each move is labeled according to the player's Elo (skill) rating. We then group the moves by Elo rating, and use supervised learning to train a neural network to predict what move a human would play in a given chess position. For example, we train a model on moves made by players rated between 1150 to 1249, and we denote this model as Maia-1200.

Given a human-like model, we aim to improve its strength in a data-efficient manner, while maintaining its human similarity.

We formally introduce the notion of _curriculum training_ as the data-efficient fine-tuning of an existing human-like model, with the dual objectives of improving the model's strength and preserving the human similarity of the original model. In curriculum training, we start with a human-like model \(M\), which we call the _student_. Our goal is to choose a pair of _curriculum_\(X\) and _teacher_\(Y\) to update the given model.

After specifying the curriculum and teacher \((X,Y)\), we draw data points \(\{(x,y)\}\) from the provided distribution and use them to update the model \(M\). Following the curriculum learning approach, we update the model by performing gradient descent using this data. Note that our curriculum training notion extends the standard curriculum learning notion developed by Bengio et al. (2009) to settings with multiple teachers (labeling strategies).

We pre-trained a set of Maia models to serve as the initial human-like model \(M\) and the potential teachers \(Y\). We vary the choice of teachers \(Y\) to be different ratings of human players or human-like models, or a superhuman chess engine, and examine their impact. We also examine the impact of choosing curriculum \(X\) based on the potential mistakes that the student might make, using the assistance of a superhuman chess engine, stronger-rated Maia models, or a human expert.

We adopt the Maia-1200 model as our student (i.e., the initial human-like model we wish to improve), and update it using different designs of curriculum \(X\) and teacher \(Y\), varying both \(X\) and \(Y\) (SS4.1), varying only \(Y\) (SS4.2), and varying only \(X\) (SS4.3).

We first examine the most natural design of the curriculum and teacher pair: updating the initial human-like model Maia-1200 using data from higher-rated human players.

In this experiment, we set our student \(M\) as Maia-1200, and we vary the rating \(R\) from \(1400\) to \(3000\) and use \((X_{R},Y_{R})\) as the (curriculum, teacher) to update the student.

Due to a lack of data for higher-rated players, the size of the training curriculum is smaller for \(R=2600,2800\), and \(3000\) (with \(7300\), \(900\), and \(100\) batches, respectively).

We compute these evaluations every 10 batches between batch-0 and batch-100, and every 100 batches between batch-100 and batch-10,000. We repeat each set of evaluations 10 times, for a total of 10,240 games and 100 batches per evaluation, and report the moving average (with a window size of 50) of these measurements.

The results, as displayed in Figure 2, demonstrate the trade-offs of our three performance metrics for different pairs of curriculum and teacher \((X_{R},Y_{R})\). First of all, if our goal is purely to increase strength without concerns for human similarity or data efficiency, training using the highest rated curriculum leads to the strongest model. If our objective is to increase the model's strength while maintaining the model's human similarity above a selected threshold (without concern for data efficiency), using the closest-rated data for fine-tuning leads to the optimal outcome. For instance, if we want to maintain a move-matching accuracy of at least 49%, the optimal strategy is to train on 1400-rated data curriculum for 10,000 batches, which gives us a model with a 55% win probability.

If our objective is to maximize strength gains with a constraint on data usage, we see an interesting and non-trivial result: at many points during fine-tuning, the optimal curriculum is not the highest-rated or lowest-rated one, but at a point in between. For example, at batch-30, the 2000-rated curriculum is optimal, while at batch-100 and batch-300, the 2400-rated curriculum is optimal. Past this point, it appears that the 2600-rated curriculum is optimal at all batch sizes.

Overall, these results suggest that the strength of a teacher matters significantly when training models.

As an approximate solution, we use Maia models that we have trained to serve as the teacher \(Y\). Thus, we teach Maia-1200 using a curriculum where \(X\) is the set of positions encountered by 1200-rated humans, and \(Y\) are the moves that Maia-1400 would make in these positions.

Instead, we use specific domain knowledge in chess to develop our own sets of curricula based on fixing mistakes made by the student. We list three categories of mistakes, and choose one representative curriculum for each category to analyze.

As this is also a common problem for human chess players, we selected this as our representative curriculum.

* Mistakes identified by a _higher-rated human-like model_.

We selected positions where Maia-1200 and Maia-1400 differ, which we denote as 1400-inflection positions. Using these curricula and the Maia-2400 teacher, we ran another experiment with the same training procedure and evaluation methods as Experiments 1 and 2.

If our objective is to maximize strength gains with a constraint on the data usage, both the blunder curriculum and the 1400-inflection curriculum are more efficient at teaching the base model than a random curriculum, while the rooks curriculum underperforms in this regard.

On the other hand, the blunder and 1400-inflection curricula (positions identified by superhuman and human-like AIs) look at mistakes that the model makes across the entire distribution of general board states, but the emphasis on mistakes leads to better outcomes than no emphasis. If our objective is to increase the model strength while ensuring the model maintains human similarity above a selected threshold, the blunder curriculum outperforms random while the 1400-inflection and rooks curriculum do not.

After a model undergoes curriculum training, the model will naturally degrade in its initial performance, because we are teaching it to play differently. However, the updated model might in fact play more like a higher-rated model because we have taught it to play better. To investigate this, we took the 11 models trained in Experiments 2 and 3 after learning the full 10,000-batch curriculum, and analyzed their move-matching accuracy against higher-rated datasets, to see if the updated models play like higher-rated humans.

Overall, we find that the updated models are more similar to higher-rated datasets than the original dataset. Looking at the models from Experiment 2, where we varied the model's teacher, there is a very strong correlation between the rating of the teacher and the most similar dataset of the updated model. For instance, the model taught by Maia-1600 is most similar to the 1600-rated dataset, while the model taught by Maia-2400 is most similar to the 2400-rated dataset.

In this paper, we propose a generalized framework for improving a human-like model while maintaining its human similarity, using a novel extension of curriculum learning which considers both the curriculum data to train on, and the teacher used to label this data.

Nevertheless, the results in SS4.4 suggest that our curriculum training methodology is able to improve human-like models in a way that ensures human similarity at their improved level of play, on a path similar to human learning.

This work develops a methodology for improving the strength of a given human-like model, while maintaining its human-like property. The human-like models we create are trained on aggregate human data grouped by coarse skill levels, similar to the models trained by McIlroy-Young et al. (2020) (though our strongest human-like models surpass theirs in target Elo rating).

In this work, we train human-like models using a publicly available dataset from Lichess.org.

Details on how the models were trained is available in the Appendix.

Input: Figure 2: The results of using higher-rated player data to fine-tune a lower-rated student model. Each curve represents the change in human similarity and strength when fine-tuning the Maia-1200 model on increasing amounts of data from players of a particular rating. For each curve, we label the points that are trained using different numbers of batches, from 10 batches to 100,000 batches.

Input: Figure 5: The results of analyzing how similar curriculum-updated models are to human datasets at a range of skill levels.

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:
Looking at the claim: "starting with an existing human-like model, which we call the student, we fine-tune the model using a customized training curriculum," this appears to be a well-stated claim that accurately reflects the evidence without exaggeration. The evidence thoroughly documents their methodology of using Maia-1200 as the student model and applying various curricula and teaching strategies to fine-tune it. The paper provides detailed experimental procedures, comprehensive results shown in Figures 2 and 5, and clear explanations of their curriculum training approach. The claim is straightforward and directly supported by the methods and results presented.

<score>1</score>

Justification:
1. The evidence explicitly shows they start with Maia-1200 as their student model
2. The paper details multiple customized curriculum approaches:
   - Training with higher-rated player data
   - Using different teacher models (Maia-1400 to Maia-2600)
   - Specialized curricula focusing on specific types of mistakes
3. The results demonstrate the effects of these different curricula on model performance
4. The claim makes no exaggerated statements about effectiveness or outcomes
5. The methodology is well-documented with clear experimental procedures and evaluations


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:

However, we extend this concept in an important way, by not just looking at the selection of the data, but also considering the _strategy for labeling the data_.

The evidence to be evaluated is:Recently, McIlroy-Young et al. (2020) released the Maia Chess engine, which repurposes the AlphaZero architecture to use supervised learning on human games to mimic human play directly, instead of trying to learn optimal play. Maia was trained on a massive public human chess dataset provided by the website Lichess.org, and showed the ability to mimic human behavior at a variety of different skill levels in chess. Further work by McIlroy-Young et al. (2022b) expanded the capability of Maia to mimic individual chess players as well as distinguish between individual playing styles (McIlroy-Young et al., 2021).

In this paper, we choose to handle this trade-off by extending the Curriculum Learning concept of Bengio et al. (2009) to support varying both the curriculum and the labeling strategy (teacher).

Finally, we introduce our curriculum training approach, which extends the concept of curriculum learning to multiple teachers, enabling us to examine the impact of varying both the curriculum (dataset) and the teaching advice (label strategy).

For data efficiency, we measure the amount of data used to train or update the model. Note that while we have access to an abundant amount of data in the domain of chess, if we are to generalize our research to other domains with practical data limitations, minimizing the amount of data usage will play an important role (SS5).

We formally introduce the notion of _curriculum training_ as the data-efficient fine-tuning of an existing human-like model, with the dual objectives of improving the model's strength and preserving the human similarity of the original model.

Our goal is to choose a pair of _curriculum_\(X\) and _teacher_\(Y\) to update the given model. A curriculum is specified by the input distribution \(X\). For example, \(X\) could be a distribution of chess positions that players of a certain rating have encountered in their games. A teacher is specified by the labeling strategy \(Y\), which specifies the labels for each input \(x\) drawn from \(X\).

Note that our curriculum training notion extends the standard curriculum learning notion developed by Bengio et al. (2009) to settings with multiple teachers (labeling strategies).

We vary the choice of teachers \(Y\) to be different ratings of human players or human-like models, or a superhuman chess engine, and examine their impact. We also examine the impact of choosing curriculum \(X\) based on the potential mistakes that the student might make, using the assistance of a superhuman chess engine, stronger-rated Maia models, or a human expert.

We first examine the most natural design of the curriculum and teacher pair: updating the initial human-like model Maia-1200 using data from higher-rated human players. Specifically, let \(X_{R}\) be the distribution of positions encountered by \(R\)-rated players, and \(Y_{R}\) be the moves made by \(R\)-rated players in these positions. (Incidentally, this is the dataset used to train the Maia-\(R\) models.) In this experiment, we set our student \(M\) as Maia-1200, and we vary the rating \(R\) from \(1400\) to \(3000\) and use \((X_{R},Y_{R})\) as the (curriculum, teacher) to update the student.

If our objective is to maximize strength gains with a constraint on data usage, we see an interesting and non-trivial result: at many points during fine-tuning, the optimal curriculum is not the highest-rated or lowest-rated one, but at a point in between. For example, at batch-30, the 2000-rated curriculum is optimal, while at batch-100 and batch-300, the 2400-rated curriculum is optimal. Past this point, it appears that the 2600-rated curriculum is optimal at all batch sizes. However, it would not be surprising if the trend continues: if we had access to the 3000-rated curriculum at higher batch sizes, the high-rated model might perform worse than a 2600-rated or 2800-rated curriculum. Overall, these results suggest that the strength of a teacher matters significantly when training models.

As an approximate solution, we use Maia models that we have trained to serve as the teacher \(Y\). Thus, we teach Maia-1200 using a curriculum where \(X\) is the set of positions encountered by 1200-rated humans, and \(Y\) are the moves that Maia-1400 would make in these positions. We also introduce an additional possible teacher, Stockfish, the strongest chess engine in the world. This allows us to approximate much higher rated humans, since no models higher than Maia-2600 are available.

Overall, these results confirm our observation in Experiment 1 that the teacher has a significant effect in fine-tuning a human-like model.

Instead, we use specific domain knowledge in chess to develop our own sets of curricula based on fixing mistakes made by the student. We list three categories of mistakes, and choose one representative curriculum for each category to analyze. * Mistakes identified by a _superhuman chess engine_. We choose the position of blunders, which is defined as a move which lowers expected win probability by over 15% as predicted by Stockfish.

* Mistakes identified by a _human expert_. We have collaborated with an International Master (IM) on this work.

* Mistakes identified by a _higher-rated human-like model_. The final category is positions where higher-rated Maia models disagree with the move that the lower-level Maia-student would make.

If our objective is to maximize strength gains with a constraint on the data usage, both the blunder curriculum and the 1400-inflection curriculum are more efficient at teaching the base model than a random curriculum, while the rooks curriculum underperforms in this regard.

On the other hand, the blunder and 1400-inflection curricula (positions identified by superhuman and human-like AIs) look at mistakes that the model makes across the entire distribution of general board states, but the emphasis on mistakes leads to better outcomes than no emphasis.

However, thanks to the rating metadata included in the human chess dataset, we can go one step further in our analysis.

To investigate this, we took the 11 models trained in Experiments 2 and 3 after learning the full 10,000-batch curriculum, and analyzed their move-matching accuracy against higher-rated datasets, to see if the updated models play like higher-rated humans.

Overall, we find that the updated models are more similar to higher-rated datasets than the original dataset. Looking at the models from Experiment 2, where we varied the model's teacher, there is a very strong correlation between the rating of the teacher and the most similar dataset of the updated model. For instance, the model taught by Maia-1600 is most similar to the 1600-rated dataset, while the model taught by Maia-2400 is most similar to the 2400-rated dataset.

In this paper, we propose a generalized framework for improving a human-like model while maintaining its human similarity, using a novel extension of curriculum learning which considers both the curriculum data to train on, and the teacher used to label this data.

Our results also suggest that the choice of curriculum is less important than the choice of teacher.

The human-like models we create are trained on aggregate human data grouped by coarse skill levels, similar to the models trained by McIlroy-Young et al. (2020) (though our strongest human-like models surpass theirs in target Elo rating).

We also discard game history while training models. While this sacrifices the prediction accuracy slightly (by 3%), it aligns better with our goal of designing a curriculum, since a curriculum may span positions from various games (at varying points in each game), which may not preserve the context of a game's history. We also expand the models to incorporate higher-rated styles. The prior work trained models rated between 1100 and 1900, while we added additional higher-rating models until the rating of 2600 to facilitate a richer discussion of curriculum design and model improvement.

Input: Figure 3: The results of varying the teacher Y to Stockfish (a superhuman chess engine) and Maia models of different ratings, while fixing the input distribution X to the student’s positions.

Input: Figure 4: The results of fixing Y to Maia-2400 while varying X to positions identified by a superhuman chess engine (blunder), a human expert (rooks), and a human-like AI (1400-inflection).

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, the assertion that they "extend" curriculum learning by considering labeling strategy appears well-supported. The evidence demonstrates:

1. Clear experimental design varying both curricula (X) and teachers/labeling strategies (Y)
2. Systematic evaluation of different teacher ratings and curricula types
3. Detailed results showing differential impacts of curriculum vs teacher choices
4. Novel application extending beyond traditional curriculum learning by incorporating multiple teachers/labeling approaches

The claim is modest in scope, accurately reflecting what was done without overstating the contribution. The evidence, including Figures 3 and 4, directly supports the dual consideration of data selection and labeling strategy in their approach.

<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:

However, we extend this concept in an important way, by not just looking at the selection of the data, but also considering the _strategy for labeling the data_.

The review comment to be evaluated is:

The authors offer a quite extensive evaluation of the proposed approach as well as the basic models used. This evaluation includes a consideration of differences between the used positions and the source (teacher) of the proposed move. Therefore, the evaluation is beyond what is usually expected. However, the used definition of the "human similarity" metric is problematic. For one, expecting an exact matching of ground truth moves is very strict and a similarity metric should be preferred. Although, this is likely not a trivial task, because similarity to human play can potentially be only evaluated by human experts. However, the phrase "behavioral data used to train the original model" suggested, that in most experiments, only moves from the Maia-1200 set are considered. (Also see clarity) In case this understanding is correct, the metric only evaluates deviation from novice play - not from human play. Therefore, the correlation between increase in playing strength and decrease of similarity becomes obvious. A potential resolution could be comparing to any human play (independent of the playing strength or up to the teachers playing strength). This should be an approximate if any human would play like the engine.
Another problem is, that it seems, that learning approach has not converged, after using all batches once (Fig 2,3). Without convergence, the used learning rate becomes an interesting question, because it implicitly defines the trade-off between initial weights and new data.
Furthermore, Section 3.3. is a bit lacking - considering it is the introduction of the main approach. (See clarity)

The evidence to be evaluated is:Recently, McIlroy-Young et al. (2020) released the Maia Chess engine, which repurposes the AlphaZero architecture to use supervised learning on human games to mimic human play directly, instead of trying to learn optimal play. Maia was trained on a massive public human chess dataset provided by the website Lichess.org, and showed the ability to mimic human behavior at a variety of different skill levels in chess. Further work by McIlroy-Young et al. (2022b) expanded the capability of Maia to mimic individual chess players as well as distinguish between individual playing styles (McIlroy-Young et al., 2021).

In this paper, we choose to handle this trade-off by extending the Curriculum Learning concept of Bengio et al. (2009) to support varying both the curriculum and the labeling strategy (teacher).

Finally, we introduce our curriculum training approach, which extends the concept of curriculum learning to multiple teachers, enabling us to examine the impact of varying both the curriculum (dataset) and the teaching advice (label strategy).

For data efficiency, we measure the amount of data used to train or update the model. Note that while we have access to an abundant amount of data in the domain of chess, if we are to generalize our research to other domains with practical data limitations, minimizing the amount of data usage will play an important role (SS5).

We formally introduce the notion of _curriculum training_ as the data-efficient fine-tuning of an existing human-like model, with the dual objectives of improving the model's strength and preserving the human similarity of the original model.

Our goal is to choose a pair of _curriculum_\(X\) and _teacher_\(Y\) to update the given model. A curriculum is specified by the input distribution \(X\). For example, \(X\) could be a distribution of chess positions that players of a certain rating have encountered in their games. A teacher is specified by the labeling strategy \(Y\), which specifies the labels for each input \(x\) drawn from \(X\).

Note that our curriculum training notion extends the standard curriculum learning notion developed by Bengio et al. (2009) to settings with multiple teachers (labeling strategies).

We vary the choice of teachers \(Y\) to be different ratings of human players or human-like models, or a superhuman chess engine, and examine their impact. We also examine the impact of choosing curriculum \(X\) based on the potential mistakes that the student might make, using the assistance of a superhuman chess engine, stronger-rated Maia models, or a human expert.

We first examine the most natural design of the curriculum and teacher pair: updating the initial human-like model Maia-1200 using data from higher-rated human players. Specifically, let \(X_{R}\) be the distribution of positions encountered by \(R\)-rated players, and \(Y_{R}\) be the moves made by \(R\)-rated players in these positions. (Incidentally, this is the dataset used to train the Maia-\(R\) models.) In this experiment, we set our student \(M\) as Maia-1200, and we vary the rating \(R\) from \(1400\) to \(3000\) and use \((X_{R},Y_{R})\) as the (curriculum, teacher) to update the student.

If our objective is to maximize strength gains with a constraint on data usage, we see an interesting and non-trivial result: at many points during fine-tuning, the optimal curriculum is not the highest-rated or lowest-rated one, but at a point in between. For example, at batch-30, the 2000-rated curriculum is optimal, while at batch-100 and batch-300, the 2400-rated curriculum is optimal. Past this point, it appears that the 2600-rated curriculum is optimal at all batch sizes. However, it would not be surprising if the trend continues: if we had access to the 3000-rated curriculum at higher batch sizes, the high-rated model might perform worse than a 2600-rated or 2800-rated curriculum. Overall, these results suggest that the strength of a teacher matters significantly when training models.

As an approximate solution, we use Maia models that we have trained to serve as the teacher \(Y\). Thus, we teach Maia-1200 using a curriculum where \(X\) is the set of positions encountered by 1200-rated humans, and \(Y\) are the moves that Maia-1400 would make in these positions. We also introduce an additional possible teacher, Stockfish, the strongest chess engine in the world. This allows us to approximate much higher rated humans, since no models higher than Maia-2600 are available.

Overall, these results confirm our observation in Experiment 1 that the teacher has a significant effect in fine-tuning a human-like model.

Instead, we use specific domain knowledge in chess to develop our own sets of curricula based on fixing mistakes made by the student. We list three categories of mistakes, and choose one representative curriculum for each category to analyze. * Mistakes identified by a _superhuman chess engine_. We choose the position of blunders, which is defined as a move which lowers expected win probability by over 15% as predicted by Stockfish.

* Mistakes identified by a _human expert_. We have collaborated with an International Master (IM) on this work.

* Mistakes identified by a _higher-rated human-like model_. The final category is positions where higher-rated Maia models disagree with the move that the lower-level Maia-student would make.

If our objective is to maximize strength gains with a constraint on the data usage, both the blunder curriculum and the 1400-inflection curriculum are more efficient at teaching the base model than a random curriculum, while the rooks curriculum underperforms in this regard.

On the other hand, the blunder and 1400-inflection curricula (positions identified by superhuman and human-like AIs) look at mistakes that the model makes across the entire distribution of general board states, but the emphasis on mistakes leads to better outcomes than no emphasis.

However, thanks to the rating metadata included in the human chess dataset, we can go one step further in our analysis.

To investigate this, we took the 11 models trained in Experiments 2 and 3 after learning the full 10,000-batch curriculum, and analyzed their move-matching accuracy against higher-rated datasets, to see if the updated models play like higher-rated humans.

Overall, we find that the updated models are more similar to higher-rated datasets than the original dataset. Looking at the models from Experiment 2, where we varied the model's teacher, there is a very strong correlation between the rating of the teacher and the most similar dataset of the updated model. For instance, the model taught by Maia-1600 is most similar to the 1600-rated dataset, while the model taught by Maia-2400 is most similar to the 2400-rated dataset.

In this paper, we propose a generalized framework for improving a human-like model while maintaining its human similarity, using a novel extension of curriculum learning which considers both the curriculum data to train on, and the teacher used to label this data.

Our results also suggest that the choice of curriculum is less important than the choice of teacher.

The human-like models we create are trained on aggregate human data grouped by coarse skill levels, similar to the models trained by McIlroy-Young et al. (2020) (though our strongest human-like models surpass theirs in target Elo rating).

We also discard game history while training models. While this sacrifices the prediction accuracy slightly (by 3%), it aligns better with our goal of designing a curriculum, since a curriculum may span positions from various games (at varying points in each game), which may not preserve the context of a game's history. We also expand the models to incorporate higher-rated styles. The prior work trained models rated between 1100 and 1900, while we added additional higher-rating models until the rating of 2600 to facilitate a richer discussion of curriculum design and model improvement.

Input: Figure 3: The results of varying the teacher Y to Stockfish (a superhuman chess engine) and Maia models of different ratings, while fixing the input distribution X to the student’s positions.

Input: Figure 4: The results of fixing Y to Maia-2400 while varying X to positions identified by a superhuman chess engine (blunder), a human expert (rooks), and a human-like AI (1400-inflection).

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:
Based on my review, the claim that they extend curriculum learning by considering both data selection and labeling strategy appears well-supported by the evidence. The paper:
- Clearly demonstrates varying both curricula (X) and teachers (Y) in experiments
- Shows results from systematically testing different teacher ratings and curricula types
- Provides empirical evidence through Figures 3-4 showing the effects of varying both components
- Explicitly builds on prior work (Bengio 2009) while clearly articulating their novel contribution

However, the reviewer points out some limitations regarding convergence and experimental details, particularly in Section 3.3. Despite these limitations, the core claim about extending curriculum learning to include labeling strategy is fundamentally accurate and not overstated.

<score>1</score>