4 papers · 1 filter
DART-ES: Difficulty-Aware Reweighting and Targeted Replay for Fine-Tuning LLMs with Evolution Strategies
Zhishen Sun, Hongzhan Wang, Sizhe Dang +2
Evolution Strategies (ES) enable memory efficient full parameter fine-tuning of large language models (LLMs) using only forward computation. However, standard ES uniformly averages…
Do Papers Tell the Whole Story? A Benchmark and Framework for Uncovering Hidden Implementation Gaps in Bioinformatics
Tianxiang Xu, Xiaoyan Zhu, Xin Lai +4
As bioinformatics software is increasingly applied across a broader range of scenarios and the rapid development of large language models (LLMs) further lowers the barriers to soft…
From to : Two-Sided Low-Rank Communication for Adam in Distributed Training with Memory Efficiency
Sizhe Dang, Jiaqi Shao, Xiaodong Zheng +3
As foundation models continue to scale, pretraining increasingly relies on data-parallel distributed optimization, making bandwidth-limited gradient synchronization a key bottlenec…
ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
Zhishen Sun, Sizhe Dang, Guang Dai +1
Reinforcement learning (RL) has become a key training step for improving mathematical reasoning in large language models (LLMs), but it often has high GPU memory usage, which makes…