首次揭示大模型强化学习对齐中的内存瓶颈并提出高效解决方案
Understanding and Alleviating Memory Consumption in RLHF for LLMs
- 系统分析RLHF中内存消耗根源，定位关键瓶颈环节
- 提出新方法使训练内存减少超50%，显著降低资源需求
- 适合需要在有限硬件上部署RLHF的开发者和研究者
使用人类反馈的强化学习（RLHF）微调是实现大语言模型对齐的关键。然而，RLHF常面临严重的内存挑战。本研究首次系统考察了RLHF场景下的内存使用情况，探索多种内存管理策略，并揭示了过度内存消耗的根本原因。此外，我们提出一种简单而有效的方案，可大幅降低RLHF微调所需的内存开销。
原文摘要 · Abstract (English)
Fine-tuning with Reinforcement Learning with Human Feedback (RLHF) is essential for aligning large language models (LLMs). However, RLHF often encounters significant memory challenges. This study is the first to examine memory usage in the RLHF context, exploring various memory management strategies and unveiling the reasons behind excessive memory consumption. Additionally, we introduce a simple yet effective approach that substantially reduces the memory required for RLHF fine-tuning.
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