AdaScale SGD: A Scale-Invariant Algorithm for Distributed Training
When using distributed training to speed up stochastic gradient descent, learning rates must adapt to new scales in order to maintain training effectiveness. Re-tuning these parameters is resource intensive, while fixed scaling rules often degrade model quality. We propose AdaScale SGD, a practical and principled algorithm that is approximately scale invariant. By continually adapting to the gradient’s variance, AdaScale often trains at a wide range of scales with nearly identical results. We describe this invariance formally through AdaScale’s convergence bounds. As the batch size increases, the bounds maintain final objective values, while smoothly transitioning away from linear speed-ups. In empirical comparisons, AdaScale trains well beyond the batch size limits of popular “linear learning rate scaling” rules. This includes large-scale training without model degradation for machine translation, image classification, object detection, and speech recognition tasks. The algorithm introduces negligible computational overhead and no tuning parameters, making AdaScale an attractive choice for large-scale training.
The paper presents and evaluate an algorithm, AdaScale SGD, to improve the stochastic gradient decent in distributed training. The proposed algorithm claims to be approximately scale-invariant, by adapting to the gradient*s variance. The paper is well-written and generally easy to read, although I didn*t check all theory in the paper. The approach is evaluated using five benchmarks (networks / dataset combinations) from different domains. The results are promising and seems solid. The paper is good and I like it, although I think the novelty and contribution is slightly too low for ICLR. The kind of tweaking and minor optimizations to provide some adaptivity (or similar) in existing and established algorithms and approaches that is presented in this is paper is very important from a practical perspective. However, from a scientific perspective it provides no significant contribution.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
AdaScale SGD: A Scale-Invariant Algorithm for Distributed Training
When using distributed training to speed up stochastic gradient descent, learning rates must adapt to new scales in order to maintain training effectiveness. Re-tuning these parameters is resource intensive, while fixed scaling rules often degrade model quality. We propose AdaScale SGD, a practical and principled algorithm that is approximately scale invariant. By continually adapting to the gradient’s variance, AdaScale often trains at a wide range of scales with nearly identical results. We describe this invariance formally through AdaScale’s convergence bounds. As the batch size increases, the bounds maintain final objective values, while smoothly transitioning away from linear speed-ups. In empirical comparisons, AdaScale trains well beyond the batch size limits of popular “linear learning rate scaling” rules. This includes large-scale training without model degradation for machine translation, image classification, object detection, and speech recognition tasks. The algorithm introduces negligible computational overhead and no tuning parameters, making AdaScale an attractive choice for large-scale training.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper presents and evaluate an algorithm, AdaScale SGD, to improve the stochastic gradient decent in distributed training. The proposed algorithm claims to be approximately scale-invariant, by adapting to the gradient*s variance. The paper is well-written and generally easy to read, although I didn*t check all theory in the paper. The approach is evaluated using five benchmarks (networks / dataset combinations) from different domains. The results are promising and seems solid. The paper is good and I like it, although I think the novelty and contribution is slightly too low for ICLR. The kind of tweaking and minor optimizations to provide some adaptivity (or similar) in existing and established algorithms and approaches that is presented in this is paper is very important from a practical perspective. However, from a scientific perspective it provides no significant contribution.
assistant
The paper presents and evaluate an algorithm, AdaScale SGD, to improve the stochastic gradient decent in distributed training. The proposed algorithm claims to be approximately scale-invariant, by adapting to the gradient*s variance. The paper is well-written and generally easy to read, although I didn*t check all theory in the paper. The approach is evaluated using five benchmarks (networks / dataset combinations) from different domains. The results are promising and seems solid. The paper is good and I like it, although I think the novelty and contribution is slightly too low for ICLR. The kind of tweaking and minor optimizations to provide some adaptivity (or similar) in existing and established algorithms and approaches that is presented in this is paper is very important from a practical perspective. However, from a scientific perspective it provides no significant contribution.