Dynamic Batch Sizes Improve Large Language Model Training Efficiency.

Traditional deep learning training often employs a static batch size, overlooking the combined impact of batch size and learning rate on the training process. This conventional approach may not be optimal for achieving peak efficiency and performance. New research delves into deep learning dynamics through the lens of convex optimization, developing a comprehensive characterization of loss that accounts for both learning rate and batch size schedules. This analysis provides a closed-form solution for an optimal batch size schedule, designed to work in conjunction with any given learning rate schedule. The findings demonstrate that these joint scaling laws, incorporating dynamic batch size schedules, consistently surpass the performance of models trained with static batch sizes. This underscores the critical role of dynamically adjusting batch sizes, particularly in the context of training large language models, for achieving superior efficiency and results.