EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training

Abstract:Training large language models (LLMs) at scale incurs substantial communication overhead, while static gradient compression cannot adapt to gradient evolution and may degrade model quality. We propose EDGC, an entropy-driven dynamic gradient compression framework that adapts compression ranks to gradient entropy during training. EDGC combines efficient entropy estimation through gradient sampling, a theoretical model relating entropy to compression rank under a bounded-error constraint, and window-based rank adjustment across pipeline stages. Experiments on 32-V100 and 64-H100 GPU clusters training GPT2 models with 2.5B and 12.1B parameters show that EDGC reduces communication latency by up to 46.45% and end-to-end training time by 16.13%, while maintaining model quality.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Performance (cs.PF)

arXiv:2511.10333 [cs.LG]

(or arXiv:2511.10333v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2511.10333 Focus to learn more arXiv-issued DOI via DataCite

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