Computer Science > Artificial Intelligence
arXiv:2603.09249 (cs)
[Submitted on 10 Mar 2026]
Title:Social-R1: Towards Human-like Social Reasoning in LLMs
Authors:Jincenzi Wu, Yuxuan Lei, Jianxun Lian, Yitian Huang, Lexin Zhou, Haotian Li, Xing Xie, Helen Meng
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Abstract:While large language models demonstrate remarkable capabilities across numerous domains, social intelligence - the capacity to perceive social cues, infer mental states, and generate appropriate responses - remains a critical challenge, particularly for enabling effective human-AI collaboration and developing AI that truly serves human needs. Current models often rely on superficial patterns rather than genuine social reasoning. We argue that cultivating human-like social intelligence requires training with challenging cases that resist shortcut solutions. To this end, we introduce ToMBench-Hard, an adversarial benchmark designed to provide hard training examples for social reasoning. Building on this, we propose Social-R1, a reinforcement learning framework that aligns model reasoning with human cognition through multi-dimensional rewards. Unlike outcome-based RL, Social-R1 supervises the entire reasoning process, enforcing structural alignment, logical integrity, and information density. Results show that our approach enables a 4B parameter model to surpass much larger counterparts and generalize robustly across eight diverse benchmarks. These findings demonstrate that challenging training cases with trajectory-level alignment offer a path toward efficient and reliable social intelligence.
| Comments: | |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09249 [cs.AI] |
| (or arXiv:2603.09249v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09249
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View a PDF of the paper titled Social-R1: Towards Human-like Social Reasoning in LLMs, by Jincenzi Wu and 7 other authors
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