Reason-guided Human Reward Modeling for Preference-based Reinforcement Learning

The paper introduces a reason-guided preference-based reinforcement learning framework that learns a human reward model from trajectory-level feedback. It augments traditional preference labels with reason tags that explain why a behavior is preferred, thereby capturing both human choices and their underlying rationale. By combining the learned human reward with the environment reward during policy optimization, the agent can improve task performance while producing more interpretable, human-aligned, and preference-sensitive behavior.

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• agents
• reinforcement-learning
• safety