Large Learning Rate

Large learning rates in neural network training are a focus of current research, aiming to understand their impact on optimization dynamics and generalization performance. Studies explore how large learning rates affect loss landscapes, influencing training trajectories and the types of solutions found, with investigations spanning various architectures and optimizers including SGD, Adam, and their variants. This research is significant because it challenges conventional wisdom about optimal learning rate strategies and offers potential for more efficient and effective training of large-scale models, particularly in resource-constrained environments.