Learning Rate

Learning rate, a crucial hyperparameter in training neural networks, dictates the step size during optimization. Current research focuses on developing adaptive learning rate schedules, such as warmup-stable-decay and learning rate path switching, to improve training efficiency and generalization, particularly for large language models and other deep learning architectures. These advancements aim to address challenges like finding optimal learning rates across varying model sizes, datasets, and training durations, ultimately leading to faster convergence and better model performance. The impact extends to various applications, from natural language processing and computer vision to scientific computing and reinforcement learning.