Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned update functions may similarly outperform current hand-designed optimizers, especially for specific tasks. However, learned optimizers are notoriously difficult to train and have yet to demonstrate wall-clock speedups over hand-designed optimizers, and thus are rarely used in practice. Typically, learned optimizers are trained by truncated backpropagation through an unrolled optimization process. The resulting gradients are either strongly biased (for short truncations) or have exploding norm (for long truncations). In this work we propose a training scheme which overcomes both of these difficulties, by dynamically weighting two unbiased gradient estimators for a variational loss on optimizer performance. This allows us to train neural networks to perform optimization faster than well tuned first-order methods. Moreover, by training the optimizer against validation loss, as opposed to training loss, we are able to use it to train models which generalize better than those trained by first order methods. We demonstrate these results on problems where our learned optimizer trains convolutional networks in a fifth of the wall-clock time compared to tuned first-order methods, and with an improvement Gradient based optimization is a cornerstone of modern machine learning. Improvements in optimization have been critical to recent successes on a wide variety of problems. In practice, this typically involves analysis and development of hand-designed optimization algorithms BID22 BID7 BID34 BID15 . These algorithms generally work well on a wide variety of tasks, and are tuned to specific problems via hyperparameter search. On the other hand, a complementary approach is to learn the optimization algorithm BID5 BID30 BID13 BID0 BID36 BID19 BID2 . That is, to learn a function to perform optimization, targeted to particular problems of interest. In this way, the algorithm may learn task specific structure, enabling dramatic performance improvements over more general optimizers. However, training learned optimizers is notoriously difficult. Existing work in this vein can be classified into two broad categories. On one hand are black-box methods such as evolution BID11 BID3 , random search BID6 , reinforcement learning BID2 BID18 BID40 , or Bayesian optimization BID31 . However, these methods scale poorly with the number of optimizer parameters. The other approach is to use first-order methods, by computing the gradient of some measure of optimizer effectiveness with respect to the optimizer parameters. Computing these gradients is costly as we need to iteratively apply the learned update rule, and then backpropagate through these applications, a technique commonly referred to as "unrolled optimization" BID4 BID21 . To address the problem of backpropagation through many optimization steps (analogous to many timesteps in recurrent neural networks), many works make use of truncated backpropagation though time (TBPTT) to partition the long unrolled computational graph into separate pieces BID35 BID33 . This yields computational savings, but at the cost of increased bias BID33 and/or exploding gradients due to many iterated update steps BID26 BID25 . Existing methods address the bias at the cost of increased variance or computa-tional complexity BID38 BID24 BID33 . Previous techniques for training RNNs via TBPTT have thus far not been effective for training optimizers. In this paper, we analytically and experimentally explore the debilitating role of bias and exploding gradients on training optimizers ( §2.3). We then show how these pathologies can be remedied by optimizing the parameters of a distribution over the optimizer parameters, known as variational optimization BID32 ) ( §3). We define two unbiased gradient estimators for this objective: a reparameterization based gradient, and evolutionary strategies BID27 BID23 . By dynamically reweighting the contribution of these two gradient estimators, we are able to avoid strongly biased or exploding gradients, and thus stably and efficiently train learned optimizers. We demonstrate the utility of this approach by training a learned optimizer to target optimization of convolutional networks on image classification ( §4). On the targeted task distribution, this learned optimizer achieves better validation loss, and is five times faster in wall-clock time, compared to well tuned hand-designed optimizers such as SGD+Momentum, RMSProp, and ADAM ( FIG0 ). While not the explicit focus of this work, we also find that the learned optimizer demonstrates promising generalization ability on out of distribution tasks ( Figure 6 ). Training and validation curves for a three layer CNN, trained on a subset of 32x32 imagenet classes not seen during outer-training of the optimizer. Dashed lines indicate the best achieved performance over an additional 130 seconds. We show two learned optimizers -one trained to minimize training loss, and the other trained to minimize validation loss on the inner-problem. We compare against Adam, RMSProp, and SGD+Momentum, individually tuned for the train and validation loss (Panel (a) and (b), respectively). On training loss (a), our learned optimizer approaches zero training loss, and achieves it's smallest loss values in less than one quarter the wall-clock time. On validation loss (b), our learned optimizer achieves a lower minimum, in roughly one third the wall-clock time. Shaded regions correspond to 25 and 75 percentile over five random initializations of the CNN. For plots showing performance in terms of step count rather than wall clock (where we achieve even more dramatic speedups), and for more task instances, see Appendix D. (c) Distribution of the performance difference between the learned optimizers and a tuned baseline of either Adam, RMSProp, or Momentum (loss improvement). Positive values indicate performance better than baseline. We show training and validation losses for the outer-testing task distribution. On the majority of tasks, the learned optimizers outperform the baseline. Our goal is to learn an optimizer which is well suited to some set of target optimization tasks. Throughout the paper, we will use the notation defined in FIG2 . Learning an optimizer can be thought of as a bi-level optimization problem BID9 , with inner and outer levels. The inner minimization consists of optimizing of the weights (w) of a target problem (w) by the repeated application of an update rule. The update rule is a parameterized function that defines how to map the weights at iteration t to iteration t + 1: w (t+1) = u(w (t) ; θ). Here, θ represents the parameters of the learned optimizer. In the outer loop, these optimizer parameters (θ) are updated so as to minimize some measure of optimizer performance, the outer-objective L(θ). Our choice T The set of tasks, where each task is a dataset (e.g., a subset of Imagenet classes). w DISPLAYFORM0 Parameters of inner-problem at iteration t. These are updated by the learned optimizer, and depend implicitly on θ and D train . DISPLAYFORM1 Loss on inner-problem, for minibatch x. θ Parameters of the optimizer. u (·; θ) Function defining the learned optimizer. The inner-loop update is DISPLAYFORM2 L train (θ) Outer-level objective targeting training loss, for L will be the average value of the target loss measured over either training or validation data. Throughout the paper, we use inner-and outer-prefixes to make it clear when we are referring to applying a learned optimizer on a target problem (inner) versus training a learned optimizer (outer). DISPLAYFORM3 In order to train an optimizer, we wish to compute derivatives of the outer-objective L with respect to the optimizer parameters, θ. Doing this requires unrolling the optimization process. That is, we can form an unrolled computational graph that consists of iteratively applying an optimizer (u) to optimize the weights (w) of a target problem FIG2 ). Computing gradients for the optimizer parameters involves backpropagating the outer loss through this unrolled computational graph. This is a costly operation, as the entire inner-optimization problem must be unrolled in order to get a single outer-gradient. Partitioning the unrolled computation into separate segments, known as truncated backpropagation, allows one to compute multiple outer-gradients over shorter segments. That is, rather than compute the full gradient from iteration t = 0 to t = T , we compute gradients in windows from t = a to t = a + τ . Below, we analyze two key problems with gradients computed via truncated unrolled optimization: bias and exploding norm. We can illustrate the problem of exploding gradients analytically with a simple example: learning a learning rate. Following the notation in FIG2 , we define the optimizer as DISPLAYFORM0 where θ is a scalar learning rate that we wish to learn for minimizing some target problem (w (t) ). For simplicity, we assume no minibatch x. Low momentum values converge to the first local minimum at w = 0. Slightly higher momentum values escape this minimum to settle at the global minimum (w ≈ .5). Even larger values oscillate before eventually settling in one of the two minimum. (c) The final loss after some number steps of optimization as a function of the momentum. The final loss surface is smooth for small number of training steps T . However, larger values of T result in near discontinuous loss surfaces around the transition points between the 2 minimum. (d) Similar to c, where the inner problem is a MLP, the learned optimizer is the one used this paper, and for a 1D slice through the outer-parameters θ along the gradient direction. (e) 2D rather than 1D slices through θ, for different numbers of innerloop steps. Intensity indicates value of L train (θ). Similar pathologies are observed to those which manifest in the toy problem. The quantity we are interested in is the outer-gradient, the derivative of the loss after T steps of gradient descent with respect to θ. We can compute this outer-gradient (see Appendix A) as: DISPLAYFORM1 where g (i) and H (j) are the gradient and Hessian of the target problem (w) at iteration i and j, respectively. We see that this equation involves a sum of products of Hessians. In particular, the first term in the sum involves a product over the entire sequence of Hessians observed during training. That is, the outer-gradient becomes a matrix polynomial of degree T , where T is the number of gradient descent steps. Thus, the outer-gradient grows exponentially with T .We can see another problem with long unrolled gradients empirically. Consider the task of optimizing a loss surface with two local minima defined as (w) = (w − 4)(w − 3)w 2 with initial condition w (0) = −1.2 using a momentum based optimizer with a parameterized momentum value θ ( FIG3 . At low momentum values the optimizer converges in the first of the two local minima, whereas for larger momentum values the optimizer settles in the second minima. With even larger values of momentum, the iterate oscillates between the two minima before settling. We visualize both the trajectory of w (t) over training and the final loss value for different momentum values in FIG3 and 3c. With increasing unrolling steps, the loss surface as a function of the momentum θ becomes less and less smooth, and develops near-discontinuities at some values of the momentum. In the case of neural network inner-problems and neural network optimizers, the outer-loss surface can grow even more complex with increasing number of unrolling steps. We illustrate this in Figure 0 5000 10000Inner problems trained In all cases, the initial learning rate is 1e − 3 (dashed line). We find low truncation amounts move away from the optimal learning rate (∼ 4e − 3). (b) Outer-gradient at each truncation over the course of inner-training. Initial outer-gradients are highly negative, trying to increase the learning rate, while later outer-gradients are slightly above zero, decreasing learning rate. The total outer-gradient is the sum of these two competing directions. (c) Cumulative value of outer-gradient vs inner-training step. When a line crosses the zero point, the outer-learned learning rate is at an equilibrium. For low unrolling steps, the cumulative sum is positive after a small number of steps, resulting in a increasing learning rate, and decreasing outer-loss. As more inner-steps are taken, the bias increases, eventually flipping the outer-gradient direction resulting in increasing outer-loss.3d and 3e for slices through the loss landscape L (θ) of the outer-problem for a neural network optimizer. Here, we demonstrate the bias introduced by using a short truncation window in unrolled optimization. These results are similar to those presented in BID40 , except that we utilize multiple truncations rather than a single, shortened unroll. First, consider outer-learning the learning rate of Adam when optimizing a small two layer neural network on MNIST(LeCun, 1998). Grid search can be used to find the optimal learning rate which is 0.004. We initialize Adam a learning rate of 0.001 and outer-train using increasing truncation amounts FIG4 ). Despite initializing close to the optimal learning rate, when outer-training with truncated backprop the resulting learning rate decreases -that is the sum of truncated outer-gradients are anti-correlated to the true outer-gradient. We visualize the per-truncation gradients of 500 steps in FIG4 and cumulative truncated gradients in FIG4 . Early in inner-training there is a large negative outer-gradient which increases the learning rate. Later in inner-training, the outer-gradients are positive, decreasing the learning rate. Thus, the optimizer parameter is pulled in opposite directions by truncated gradients early versus late in inner-training, revealing an inherent tension with truncated unrolled optimization. To perform outer-optimization of a loss landscape with high frequency structure like that in FIG3 , one might intuitively want to smooth the outer-objective loss surface. To do this, instead of optimizing L(θ) directly, we instead optimize a smoothed outer-loss L (θ), DISPLAYFORM0 where σ 2 is a fixed variance (set to 0.01) which determines the degree of smoothing. This is the same approach taken in variational optimization BID32 . We can construct two different unbiased gradient estimators for L (θ): one via the reparameterization trick BID16 , and one via the log-derivative trick similar to what is done in evolutionary strategies (ES) BID37 . We denote the two estimates as g rp and g es respectively, Following the insight from BID25 in the context of reinforcement learning 1 , we combine these estimates using inverse variance weighting BID8 , DISPLAYFORM1 DISPLAYFORM2 where σ 2 rp and σ 2 es are the empirical variances of g rp and g es respectively. When outer-training learned optimizes we find the variances of g es and g rp can differ by as many as 20 orders of magnitude FIG5 ). This merged estimator addresses this by having at most the lowest of the two variances. To further reduce variance, we use antithetic sampling when estimating g es .The cost of computing a single sample of g es and g rp is thus 2 forward and backward passes of an unrolled optimization. To compute the empirical variance, we leverage data parallelism to compute multiple samples of g es and g rp . In theory, the samples used to evaluate σ 2 rp , σ 2 es must be independent of g es and g rp , but in practice we found good performance using the same samples for both. Finally, an increasing curriculum over steps per truncation is used over the course of outer-training. This introduces bias early in training, but also allows for far more frequent outer-weight updates, resulting in much faster outer-training in terms of wall-clock time. The full outer-training algorithm is described in Appendix B. The optimizer architecture used in all experiments consists of a simple fully connected neural network, with one hidden layer containing 32 ReLU units. This network is applied to each target problem weight independently. The outputs of the MLP consist of an update direction and a log learning rate, which are combined to produce weight updates. The network for each weight takes as input: the gradient with respect to that weight, parameter value, RMS gradient terms BID20 , exponentially weighted moving averages of gradients at multiple time scales, as well as a representation of the current iteration. Many of these input features were motivated by BID36 . We conduct ablation studies for these inputs in §4.5. See Appendix C for further architectural details. The problem that each learned optimizer is trained against consists of training a three layer convolutional neural network (32 units per layer) inner-trained for ten thousand inner-iterations on 32x32x3 image classification tasks. We split the Imagenet dataset (Russakovsky et al., 2015) by class into Figure 6: Despite outer-training only targeting optimizer performance on a three layer convolutional network trained on 10 way classification problems derived from 32x32 Imagenet, the learned optimizers demonstrate some ability to generalize to new architectures and datasets. Plot shows train and validation performance on a six layer convolutional neural network trained on 28x28 MNIST. We compare against hyperparameter tuned Adam, RMSProp, and SGD+Momentum. Note that the optimizer trained to target validation loss generalizes better than the one trained to target train loss. See Appendix E for experiments testing generalization to additional tasks. 700 training and 300 validation classes, and sample training and validation problems by sampling 10 classes at random using all images from each class. This experimental design lets the optimizer learn problem specific structure (e.g. convolutional networks trained on object classification), but does not allow the optimizer to memorize particular weights for the base problem. See Appendix C for further details. To train the optimizer, we linearly increase the number of unrolled steps from 50 to 10,000 over the course of 5,000 outer-training weight updates. The number of unrolled steps is additionally jittered by a small percentage (sampled uniformly up to 20%). Due to the heterogeneous, small workloads, we train with asynchronous batched SGD using 128 CPU workers. FIG5 shows the performance of the optimizer (averaged over 40 randomly sampled outer-train and outer-test inner-problems) while outer-training. Despite the stability improvements described in the last section, there is still variability in optimizer performance over random initializations of the optimizer parameters. We use training loss to select the best model and use this in the remainder of the evaluation. FIG0 shows performance of the learned optimizer, after outer-training, compared against other first-order methods on a sampled validation task (classes not seen during outer-training). For all first-order methods, we report the best performance after tuning the learning rate by grid search using 11 values over a logarithmically spaced range from 10 −4 to 10. When outer-trained against the training outer-objective, L train , we achieve faster convergence on training loss by a factor of 5x FIG0 ), but poor performance on validation loss FIG0 . However, when outer-trained against the validation outer-objective, L valid , we also achieve faster optimization and reach a lower validation loss FIG0 . FIG0 summarizes the performance of the learned optimizer across many sampled validation tasks. It shows the difference in final validation loss between the best first-order method and the learned optimizer. We choose the best first-order method by selecting the best validation performance over RMSProp, SGD+Momentum, and Adam. This learned optimizer (which does not require tuning on the validation tasks) outperforms the best baseline optimizer 98% of the time. Although the focus of our approach was not generalization, we find that our learned optimizer nonetheless generalizes to varying degrees to dissimilar datasets, different numbers of units per layer, different number of layers, and even to fully connected networks. In Figure 6 we show performance on a six layer convolutional neural network trained on MNIST. Despite the different number of layers, different dataset, and different input size, the learned optimizers still reduces the loss, B a s e li n e (n = 1 2 ) G ra d ie n ts (n = 9 ) E S (n = 9 ) 1 0 u n ro ll (n = Outer-objective Figure 7 : We compare the model described in §4.1 with different features removed. Shown above is the distribution of outer-loss performance across random seed averaged between 380k and 420k inner problem trained. For full learning curves, see Appendix G. First, with regard to gradient estimators we find the combined estimator is more stable and out performs both the analytic gradient, and evolutionary strategies. With regard to a curriculum, we find poor convergence with lower numbers of unrolling steps (10, 100) and high variance with larger numbers of truncation's steps (1000). Finally with regard to optimizer features, we find our learned optimizers perform nearly as well without the RMS terms and without the time horizon input, but fails to converge when not given access to momentum based features.8 and in the case of the validation outer-objective trains faster to a lower validation loss. We further explore the limits of generalization of our learned optimizer on additional tasks in Appendix E. To assess the importance of the gradient estimator discussed in §3, the unrolling curriculum §4.3, as well as the features fed to the optimizer enumerated in §4.1, we re-trained the learned optimizer removing each of these additions. In particular, we trained optimizers with: only the analytic gradient (Only Gradients), only with evolutionary strategies (Only ES), a fixed number unrolled steps (10, 100, 1000) as opposed to a schedule, no RMS gradient scaling (No RMS), no momentum terms (No Mom), no momentum and no RMS scaling (Only Grads), and without the current iteration (No Time). Figure 7 summarizes these findings, showing the learned optimizer performance for each of these ablations. We find that the gradient estimator (in §3) and a increasing schedule of unroll steps are critical to performance, along with including momentum as an input to the optimizer. In this work we demonstrate two difficulties when training learned optimizers: "exploding" gradients, and a bias introduced by truncated backpropagation through time. To combat this, we construct a variational bound of the outer-objective and minimize this via a combination of reparameterization and ES style gradient estimators. By using our combined estimator and a curriculum over truncation step we are able to train learned optimizers that achieve more than five times speedup on wallclock time as compared to existing optimizers. In this work, we focused on applying optimizers to a restricted family of tasks. While useful on its own right (e.g. rapid retraining of models on new data), future work will explore the limits of "no free lunch" BID39 to understand how and when learned optimizers generalize across tasks. We are also interested in using these methods to better understand what problem structure our learned optimizers exploit. By analyzing the trained optimizer, we hope to develop insights that may transfer back to hand-designed optimizers. Outside of meta-learning, we believe the gradient estimator presented here can be used to train other long time dependence recurrent problems such as neural turning machines BID12 , or neural GPUs BID14 .Much in the same way deep learning has replaced feature design for perceptual tasks, we see metalearning as a tool capable of learning new and interesting algorithms, especially for domains with unexploited problem-specific structure. With better outer-training stability, we hope to improve our ability to learn interesting algorithms, both for optimizers and beyond. After T steps of gradient descent, the final loss becomes (w (T ) ). We can update the parameters of the optimizer (θ) by computing the derivative of this final loss with respect to θ: DISPLAYFORM0 dθ . The second term in this expression can be defined recursively as: dw DISPLAYFORM1 where ∇ and ∇ 2 are the gradient and Hessian of the target problem, respectively. This expression highlights where the exploding outer-gradient comes from: the recursive definition of DISPLAYFORM2 dθ means that computing it will involve a product of the Hessian at every iteration. Initialize outer-parameters (θ). while Outer-training, for each parallel worker do Sample a dataset D, from T . Initialize the inner loop parameters w (0) randomly. for Each truncation in the inner loop do Sample ES perturbation: e ∼ N (0, σ 2 I). Sample a number of steps per truncation, k, based on current outer-training index. Compute a positive, and negative sequence starting from w (t) by iteratively applying (for k steps), u(·; θ + e), to w DISPLAYFORM0 Compute a pair of outer-objectives L + , and L − using the 2 sequences of w from t to k using either the train or validation inner-problem data. Compute a single sample of DISPLAYFORM1 θ, σ 2 I)) Store the sample of (g rp , g es ) in a buffer until a batch of samples is ready. Assign the final w from one of the two sequences to w (t+k) . end for end while while Outer-training and in parallel do When a batch of gradients is available, compute empirical variance and empirical mean of each weight for each estimator. Use equation 1 to compute the combined gradient estimate. update meta-parameters with SGD: θ ← θ − αg combined where α is a learning rate. end while Algorithm 1: Outer-training algorithm using the combined gradient estimator. C ARCHITECTURE DETAILS C.1 ARCHITECTURE In a similar vein to diagonal preconditioning optimizers, and existing learned optimizers our architecture operates on each parameter independently. Unlike other works, we do not use a recurrent model as we have not found applications where the performance gains are worth the increased computation. We instead employ a single hidden layer feed forward MLP with 32 hidden units. This MLP takes as input momentum terms, as well as rms terms inspired by Adam computed at a few different decay values: [0.5, 0.9, 0.99, 0.999, 0.9999] BID36 . A similar idea has been explored with regard to momentum parameters in BID20 . We also pass in 2 terms: r = 1 rms + and rms also from computations performed in Adam. Despite being critical in BID36 we find these features of minimal impact (see §4.5). This is 5 · 4 = 20 features in total. The current gradient as well as the current weight value are also used as features. By passing in weight values, the optimizer can learn to do arbitrary norm weight decay. To emulate learning rate schedules, the current training iteration is fed in transformed via applying a tanh squashing functions at different timescales: tanh(t/η − 1) where η is the timescale. We use 9 timescales logarithmicly spaced from (3, 300k) . This leaves us in total with 31 features. All non-time features are normalized by the second moment with regard to other elements in the "batch" dimension (the other weights of the weight tensor). We choose this over other normalization strategies to preserve directionality. These activations are then passed the into a hidden layer, 32 unit MLP with relu activations. Many existing optimizer hyperparameters (such as learning rate) operate on an exponential scale. As such, the network produces two outputs, and we combine them in an exponential manner: exp(λ exp o 1 )λ lin o 2 making use of two temperature parameters λ exp and λ lin which are both set to 1e−3. Without these scaling terms, the default initialization yields steps on the order of size 1 -far above the step size of any known optimizer and result in highly chaotic regions of θ. It is still possible to optimize given our estimator, but training is slow and the solutions found are quite different. The outer-training set consists of a family of 10 way classification problems using cross entropy loss on subsets of 32x32 Imagenet. To form a outer-train and outer-test set, we randomly split imagenet into 700 classes for outer-train, and 300 classes for outer-test. The optimizer targets a 3 layer convolutional neural network with 3x3 kernels, and 32 units per layer. The first 2 layers are stride 2, and the 3rd layer has stride 1. We use relu activations and glorot initializations BID10 . At the last convolutional layer, an average pool is performed, and a linear projection is applied to get the 10 output classes. We train using the algorithm described in 1 using a linear schedule on the number of unrolling steps from 50 -10k over the course of 5k outer-training iterations. To add variation in length, we additionally shift this length by a percentage uniformly sampled between (-20%, 20%). We optimize the outer-parameters, θ, using Adam (Kingma & Ba, 2014) with a batch size of 128 and with a learning rate of 0.003 for the training outer-objective and 0.0003 for the validation outer-objective, and β 1 = 0.5(following existing literature on non-stationary optimization BID1 ).While both values of learning rate work for both outer-objectives, we find the validation outerobjective to be considerably harder, and training is more stable with the lower learning rate. We plot additional learning curves from both the outer-train task distribution and the outer-validation task distribution. The horizontal lines represent the minimum performance achieved over 20k steps. See Figure In this work, we focus our attention to learning optimizers over a specific task distribution (3 layer convolutional networks trained on 10 way subsets of 32x32 Imagenet). In addition to testing on these in domain problems (Appendix D), we test our learned optimizer on a variety of out of domain target problems. Despite little variation in the outer-training task distribution, our models show promising generalization when transferred to a wide range of different architectures (fully connected, convolutional networks) depths (2 layer to 6 layer) and number of parameters (models roughly 16x more parameters). We see these as promising sign that our learned optimizer has a reasonable but not perfect inductive bias. We leave training with increased variation to encourage better generalization as an area for future work. When training models, often one cares about taking less wallclock time as compared to performance per weight update. Much like existing first order optimizers, the computation performed in our learned optimizer is linear in terms of number of parameters in the model being trained and smaller than the cost of computing gradients. The bulk of the computation in our model consists of two batched matrix multiplies of size 31x32, and 32x2. When training models that make use of weight sharing, e.g. RNN or CNN, the computation performed per weight often grows super linearly with model size. As the learned optimizer methods are scaled up, the additional overhead in performing more complex weight updates will vanish. For the specific models we test in this paper, we measure the performance of our optimizer on CPU and GPU. We implement Adam, SGD, and our learned optimizer in TensorFlow for this comparison. Given the small scale of problem we are working at, we implement training in graph in a tf.while loop to avoid TensorFlow Session overhead. We use random input data instead of real data to avoid any data loading confounding. On CPU the learned optimizer executes at 80 batches a second where Adam runs at 92 batches a second and SGD at 93 batches per second. The learned optimizer is 16% slower than both. On a GPU (Nvidia Titan X) we measure 177 batches per second for the learned and 278 batches per second for Adam, and 358 for sgd. This is or 57% slower than adam and 102% slower than SGD.Overhead is considerably higher on GPU due to the increased number of ops, and thus kernel executions, sent to the GPU. We expect a fused kernel can dramatically reduce this overhead. Despite the slowdown in computation, the performance gains (greater than 400% faster in steps) far exceed the slowdown, resulting in an optimizer that is still considerably faster when measured in wallclock time.
We analyze problems when training learned optimizers, address those problems via variational optimization using two complementary gradient estimators, and train optimizers that are 5x faster in wall-clock time than baseline optimizers (e.g. Adam). This paper uses un-rolled optimization to learn neural networks for optimization. This paper tackles the problem of learning an optimizer, specifically the authors focus on obtaining cleaner gradients from the unrolled training procedure. Presents a method for "learning an optimizer" by using a Variational Optimization for the "outer" optimizer loss and proposes the idea to combine both the reparametrized gradient and the score-function estimator for the Variational Objective and weights them using a product of Gaussians formula for the mean.