A Proof of Learning Rate Transfer under $\mu$P

A recent study has made a significant breakthrough in understanding learning rate transfer in neural networks, specifically in multi-layer perceptrons (MLPs) using a parameterization called μP. This research demonstrates that as the width of the network increases, the optimal learning rate stabilizes to a non-zero constant, which could enhance the efficiency of training deep learning models. This finding is crucial as it provides a theoretical foundation for optimizing learning rates, potentially leading to better performance in various applications of artificial intelligence.