ARC Restores Fairness in Open-Ended Agentic Reinforcement Learning
August 12, 2026
ARC (Advantage Regularization via Conditioning) mitigates reward bias in open-ended interactions where agents have multiple valid behavioral styles. It uses strategy-conditioned rollout grouping to prevent reward models from favoring specific interaction styles over task effectiveness.
HOW THIS AFFECTS YOU
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researcherThis provides a training recipe to ensure RL agents optimize for context-appropriate behavior rather than reward hacking.