PILAF: Optimal Human Preference Sampling for Reward Modeling

TL;DR

PILAF introduces a theoretically grounded sampling strategy for preference labeling in RLHF, improving alignment with true human values and enhancing reward model training for large language models.

Contribution

The paper proposes PILAF, a novel, optimal response sampling method that explicitly aligns preference learning with maximizing the underlying oracle reward in RLHF.

Findings

Demonstrates strong performance in iterative RLHF settings

Provides theoretical guarantees of optimality

Easily implementable and effective in practice

Abstract

As large language models increasingly drive real-world applications, aligning them with human values becomes paramount. Reinforcement Learning from Human Feedback (RLHF) has emerged as a key technique, translating preference data into reward models when oracle human values remain inaccessible. In practice, RLHF mostly relies on approximate reward models, which may not consistently guide the policy toward maximizing the underlying human values. We propose Policy-Interpolated Learning for Aligned Feedback (PILAF), a novel response sampling strategy for preference labeling that explicitly aligns preference learning with maximizing the underlying oracle reward. PILAF is theoretically grounded, demonstrating optimality from both an optimization and a statistical perspective. The method is straightforward to implement and demonstrates strong performance in iterative and online RLHF settings where feedback curation is critical.