Policy-labeled Preference Learning: Is Preference Enough for RLHF?

2025-05-06

AI Summary

Existing RLHF methods erroneously assume human-labeled trajectories are generated by an optimal policy, leading to biased trajectory likelihood estimation and suboptimal policy learning. To address this, we propose Preference Learning with Policy Annotation (PPL), the first framework to explicitly model regret in preference learning—thereby recovering behavioral policy information and calibrating the trajectory distribution. Our key contributions are: (1) a regret-based preference likelihood model that mitigates likelihood misestimation; and (2) a derived contrastive KL regularization term that enhances policy stability and alignment in sequential decision-making. Evaluated on high-dimensional continuous control tasks, PPL significantly improves offline RLHF performance and demonstrates strong generalization and robustness in online RLHF settings.

Technology Category

Humans and AI: Learning Human Values and PreferencesMachine Learning: Learning Preferences or RankingsReasoning under Uncertainty: Sequential Decision Making

Application Category

Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labelingSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

To design rewards that align with human goals, Reinforcement Learning from Human Feedback (RLHF) has emerged as a prominent technique for learning reward functions from human preferences and optimizing policies via reinforcement learning algorithms. However, existing RLHF methods often misinterpret trajectories as being generated by an optimal policy, causing inaccurate likelihood estimation and suboptimal learning. Inspired by Direct Preference Optimization framework which directly learns optimal policy without explicit reward, we propose policy-labeled preference learning (PPL), to resolve likelihood mismatch issues by modeling human preferences with regret, which reflects behavior policy information. We also provide a contrastive KL regularization, derived from regret-based principles, to enhance RLHF in sequential decision making. Experiments in high-dimensional continuous control tasks demonstrate PPL's significant improvements in offline RLHF performance and its effectiveness in online settings.

Problem

• Resolves likelihood mismatch in RLHF by modeling preferences with regret
• Introduces contrastive KL regularization for better sequential decision making
• Improves offline and online RLHF performance in control tasks

Innovation

• Policy-labeled preference learning with regret modeling
• Contrastive KL regularization for sequential decisions
• Resolves likelihood mismatch in RLHF methods