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EvoRS is introduced, a self-evolving RL framework that evolves the reward system from on-policy experience, representing it as an executable Reward-DAG, and is updated from on-policy rollouts and reward traces to maintain train-time reliability.
This work proposes SocialRL, a multi-turn reinforcement learning framework using PPO that propagates delayed outcome rewards back to each turn, enabling long-horizon planning and demonstrates the effectiveness of SocialRL across synthetic and real social scenes, as well as standard and challenging social scenarios.
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