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arXiv · 2410.15651

Understanding and Alleviating Memory Consumption in RLHF for LLMs

Jin Zhou·Hanmei Yang·Steven·Tang·Mingcan Xiang·Hui Guan·Tongping Liu
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Abstract

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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Jin Zhou, Hanmei Yang, Steven, Tang, Mingcan Xiang, Hui Guan, Tongping Liu. 2024-10-21. Understanding and Alleviating Memory Consumption in RLHF for LLMs. https://arxiv.org/abs/2410.15651

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