PILAF: Optimal Human Preference Sampling for Reward Modeling
Authors: name: Yunzhen Feng, name: Ariel Kwiatkowski, name: Kunhao Zheng, name: Julia Kempe, name: Yaqi Duan

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.