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SocialRL: Refining LLMs' Social Intelligence through Multi-turn Reinforcement Learning and Reward Design

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

Social intelligence enables agents to read social context, infer intent, and adapt over sustained dialogue. As language models become autonomous collaborators, it is central to building effective and trustworthy human-AI interaction. Existing reinforcement learning methods optimize single-turn utterances and sparse outcome rewards, producing short-sighted policies that struggle to manage goal-relationship tensions across multi-turn interactions. We propose SocialRL, a multi-turn reinforcement learning framework addressing both challenges. First, we apply multi-turn reinforcement learning using

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