Efficient Reinforcement Learning from Human Feedback via Bayesian Preference Inference

• URL: http://arxiv.org/abs/2511.04286v1
• Date: Thu, 06 Nov 2025 11:27:38 GMT
• Title: Efficient Reinforcement Learning from Human Feedback via Bayesian Preference Inference
• Authors: Matteo Cercola, Valeria Capretti, Simone Formentin,
• Abstract summary: We propose a hybrid framework that unifies RLHF's scalability with PBO's query efficiency.<n>We validate the proposed approach on two representative domains: (i) high-dimensional preference optimization and (ii) LLM fine-tuning.
• Score: 0.29057513016551245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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