Understanding and Alleviating Memory Consumption in RLHF for LLMs

TL;DR

This paper investigates memory issues in RLHF for LLMs, analyzes causes, and proposes a simple method to significantly reduce memory usage during fine-tuning.

Contribution

It is the first study to analyze memory consumption in RLHF for LLMs and introduces an effective approach to mitigate high memory requirements.

Findings

Identified key factors causing high memory usage in RLHF

Proposed a simple method that reduces memory consumption substantially

Provided insights into memory management strategies for RLHF

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.