Learning Rate Schedule

Learning rate schedules, which dynamically adjust the step size during model training, are crucial for optimizing the performance of machine learning algorithms, particularly in large language models and deep neural networks. Current research focuses on developing theoretically grounded schedules, analyzing their impact on generalization and convergence (especially for non-convex problems), and designing adaptive methods that automatically adjust the learning rate based on observed training dynamics. These advancements aim to improve training efficiency, reduce computational costs, and enhance model performance across various applications, from traditional machine learning tasks to large-scale language model fine-tuning.