Lion Cub: Minimizing Communication Overhead in Distributed Lion

Communication overhead is a key challenge in distributed deep learning, especially on lower-bandwidth interconnects, and given current hardware trends, communication is likely to become a major bottleneck.While gradient compression techniques have been explored for SGD and Adam, the Lion optimizer has the distinct advantage that its update vectors are the output of a sign operation, enabling straightforward quantization.However, simply compressing updates for communication and using techniques like majority voting fails to lead to end-to-end speedups due to inefficient communication algorithms and reduced convergence.We analyze three factors critical to distributed learning with Lion: optimizing communication methods, identifying effective quantization methods, and assessing the necessity of momentum synchronization.Our findings show that quantization techniques adapted to Lion and selective momentum synchronization can reduce communication costs while maintaining convergence.We combine these into Lion Cub, which enables speedups in training compared to Lion especially with low-bandwidth environments.This highlights Lion's potential as a communication-efficient solution for distributed training.