Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning

• URL: http://arxiv.org/abs/2504.03784v3
• Date: Tue, 15 Apr 2025 09:29:06 GMT
• Title: Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning
• Authors: Kai Ye, Hongyi Zhou, Jin Zhu, Francesco Quinzan, Chengchung Shi,
• Abstract summary: Reinforcement learning from human feedback (RLHF) has emerged as a key technique for aligning the output of large language models with human preferences.<n>Most existing RLHF algorithms use the Bradley-Terry model, which relies on assumptions about human preferences that may not reflect the complexity and variability of real-world judgments.<n>We propose a robust algorithm to enhance the performance of existing approaches under such reward model misspecifications.
• Score: 3.30671592417223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward function, most existing RLHF algorithms use the Bradley-Terry model, which relies on assumptions about human preferences that may not reflect the complexity and variability of real-world judgments. In this paper, we propose a robust algorithm to enhance the performance of existing approaches under such reward model misspecifications. Theoretically, our algorithm reduces the variance of reward and policy estimators, leading to improved regret bounds. Empirical evaluations on LLM benchmark datasets demonstrate that the proposed algorithm consistently outperforms existing methods, with 77-81% of responses being favored over baselines on the Anthropic Helpful and Harmless dataset.

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