关于将大型语言模型与RLHF对齐的算法偏差:偏好崩和匹配规范化
Jiancong Xiao1, Ziniu Li2, Xingyu Xie3
1University of Pennsylvania.
Journal of the American Statistical Association
|January 26, 2026
概括
从人类反 (RLHF) 进行强化学习可以偏向大型语言模型 (LLM). 一种新的方法,偏好匹配RLHF,可以证明LLMs与人类偏好保持一致,提高公平性和减少偏见.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 将大型语言模型 (LLM) 与人类偏好保持一致对于决策至关重要.
- 目前从人类反 (RLHF) 方法的强化学习可能会引入算法偏见,可能会忽视少数群体的偏好 (偏好崩).
研究的目的:
- 引入一种新的方法,即偏好匹配 (PM) RLHF,可以减轻LLM对齐中的算法偏差.
- 通过使用布拉德利-特里-卢斯/普拉克特-卢斯模型,可证明地将LLM与奖励模型的偏好分布对齐.
主要方法:
- 开发了一个PM调节器 (LLM的政策概率分布的负对数),以平衡响应多样化和奖励最大化.
- 通过解决一个普通微分方程来导出PM调节器.
- 引入了PM RLHF的条件变体,用于自然语言生成.
主要成果:
- 有条件的PM RLHF在与人类偏好保持一致方面表现出显著的改善.
- 与标准RLHF相比,对OPT和拉玛家族模型的实验显示了29%至41%的改善.
结论:
- 偏好匹配RLHF提供了一种可证明有效的方法,用于减轻LLM对齐中的算法偏差.
- 有条件的PM RLHF方法提高了公平性和准确性,使LLM与人类偏好保持一致,特别是在自然语言生成任务中.
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