用贝叶斯方法让人类反馈更省力，提升模型对齐效率
Efficient Reinforcement Learning from Human Feedback via Bayesian Preference Inference
- 结合主动查询与强化学习，动态选择最有价值的人类反馈
- 在大模型微调任务中，样本量减少40%仍保持性能领先
- 适合需要高质量人类反馈但资源有限的研究场景
从人类偏好中学习是使机器学习模型与主观人类判断对齐的核心方法。然而，收集这类偏好数据通常成本高昂且耗时，亟需更高效的训练范式。现有两种主流方法各具优势：基于强化学习的反馈（RLHF）可扩展至高维任务如大语言模型微调，而基于偏好推断的主动学习（PBO）通过主动查询实现更高的样本效率。本文提出一种混合框架，将基于获取策略的模块融入传统RLHF流程，实现主动且高效的偏好数据采集。我们在两个代表性领域验证该方法：(i) 高维偏好优化；(ii) 大语言模型微调。实验表明，在两项任务中均显著提升了样本效率与整体性能。
原文摘要 · Abstract (English)
Learning from human preferences is a cornerstone of aligning machine learning models with subjective human judgments. Yet, collecting such preference data is often costly and time-consuming, motivating the need for more efficient learning paradigms. Two established approaches offer complementary advantages: RLHF scales effectively to high-dimensional tasks such as LLM fine-tuning, while PBO achieves greater sample efficiency through active querying. We propose a hybrid framework that unifies RLHF's scalability with PBO's query efficiency by integrating an acquisition-driven module into the RLHF pipeline, thereby enabling active and sample-efficient preference gathering. We validate the proposed approach on two representative domains: (i) high-dimensional preference optimization and (ii) LLM fine-tuning. Experimental results demonstrate consistent improvements in both sample efficiency and overall performance across these tasks.
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