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Reason-Guided Human Reward Modeling for Preference-Based Reinforcement Learning

This paper proposes a reason-guided preference-based reinforcement learning (PbRL) framework that learns a human reward model from trajectory-level feedback, introducing reason tags that explain why a behavior is preferred in addition to preference labels. Compared with preference-only reward learning, this method provides additional supervision for learning human-aligned rewards and improves the interpretability of the policy. The learned human reward is combined with the environment reward for policy optimization, enabling the agent to improve task performance while producing more interpretable behavior that better matches human preferences and aligns with human intent.

Part of the event: A Rationale-Guided Framework for Human Reward Modeling

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