What do near-optimal learning rate schedules look like?
arXiv:2603.10301v1 Announce Type: new Abstract: A basic unanswered question in neural network training is: what is the best learning rate schedule shape for a given workload? The choice of learning rate schedule is a key factor in the success or failure of the training process, but beyond having some kind of warmup and decay, there is no consensus on what makes a good schedule shape. To answer this question, we designed a search procedure to find the best shapes within a parameterized schedule family. Our approach factors out the schedule shape from the base learning rate, which otherwise would dominate cross-schedule comparisons. We applied our search procedure to a variety of schedule families on three workloads: linear regression, image classification on CIFAR-10, and small-scale language modeling on Wikitext103. We showed that our search procedure indeed generally found near-optimal schedules. We found that warmup and decay are robust features of good schedules, and that commonly
arXiv:2603.10301v1 Announce Type: new Abstract: A basic unanswered question in neural network training is: what is the best learning rate schedule shape for a given workload? The choice of learning rate schedule is a key factor in the success or failure of the training process, but beyond having some kind of warmup and decay, there is no consensus on what makes a good schedule shape. To answer this question, we designed a search procedure to find the best shapes within a parameterized schedule family. Our approach factors out the schedule shape from the base learning rate, which otherwise would dominate cross-schedule comparisons. We applied our search procedure to a variety of schedule families on three workloads: linear regression, image classification on CIFAR-10, and small-scale language modeling on Wikitext103. We showed that our search procedure indeed generally found near-optimal schedules. We found that warmup and decay are robust features of good schedules, and that commonly used schedule families are not optimal on these workloads. Finally, we explored how the outputs of our shape search depend on other optimization hyperparameters, and found that weight decay can have a strong effect on the optimal schedule shape. To the best of our knowledge, our results represent the most comprehensive results on near-optimal schedule shapes for deep neural network training, to date.
Executive Summary
This article presents a novel approach to determining the optimal learning rate schedule shape for deep neural network training. The authors develop a search procedure that factors out the base learning rate, allowing for a more nuanced understanding of the schedule shape's impact on training success. The study applies this procedure to various workloads and schedule families, revealing that warmup and decay are essential features of good schedules. Notably, the authors find that commonly used schedule families are suboptimal in certain contexts. The article's findings highlight the importance of considering schedule shape when designing neural network training protocols and suggest that optimal schedules may depend on specific optimization hyperparameters.
Key Points
- ▸ Developed a search procedure to find near-optimal learning rate schedule shapes
- ▸ Factored out base learning rate to isolate schedule shape's impact
- ▸ Warmup and decay are robust features of good schedules
- ▸ Commonly used schedule families are not optimal on various workloads
Merits
Strength in Methodology
The authors' use of a search procedure to find near-optimal schedule shapes is a significant methodological advancement, enabling the isolation of schedule shape's impact on training success.
Robustness of Warmup and Decay
The finding that warmup and decay are essential features of good schedules provides valuable insights into the design of effective learning rate schedules.
Demerits
Limited Generalizability
The study's focus on specific workloads and schedule families may limit the generalizability of its findings to other contexts.
Oversimplification of Hyperparameter Interactions
The authors' exploration of weight decay's impact on optimal schedule shapes may oversimplify the complex interactions between hyperparameters.
Expert Commentary
This article represents a significant contribution to the field of deep learning, providing a more nuanced understanding of the learning rate schedule's impact on training success. The authors' search procedure is a valuable tool for researchers and practitioners seeking to optimize their neural network training protocols. However, the study's limitations, particularly with regards to generalizability and hyperparameter interactions, highlight the need for further research in this area. As the field continues to evolve, it is essential to consider the complex interactions between hyperparameters and schedule shape to develop more effective and generalizable learning rate scheduling strategies.
Recommendations
- ✓ Future research should investigate the generalizability of the study's findings to other workloads and schedule families.
- ✓ A more comprehensive exploration of hyperparameter interactions, including weight decay and other optimization hyperparameters, is necessary to fully understand the impact of schedule shape on training success.