Title: Social-R1: Enhancing Social Intelligence in LLMs through Human-like Reinforced Reasoning
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Large Language Model, Social Intelligence, Theory of Mind, Reinforcement Learning

Abstract: Recent advances in reinforcement learning with verifiable rewards (RLVF) have elicited strong reasoning abilities in large language models (LLMs) on objective tasks such as math and coding, yet social intelligence—the capacity to perceive social cues, infer others’ mental states, and interact effectively—remains underexplored. We argue that progress has been hindered by the simplicity and homogeneity of existing social datasets, which incentivize shortcut solutions over genuine Theory-of-Mind (ToM) reasoning. To address this, we introduce \textbf{ToMBench-Hard}, a challenging, multi-dimensional multiple-choice benchmark that rigorously evaluates ToM (e.g., perspective-taking, belief revision, and deception), exposes limitations of current LLMs, and provides verifiable outcomes for reinforcement learning. Training with RLVF on ToMBench-Hard using only outcome-based rewards already yields clear improvements. Motivated by the role of human-like mental processes in social cognition, we further collect diverse reasoning trajectories and train a social thinking reward model that scores trajectory quality—rewarding accurate perception of social cues and ToM-consistent inference prior to answer generation. We combine these signals in \textbf{Social-R1}, a reinforcement learning framework for social reasoning that integrates outcome and trajectory-level rewards. Across SocialIQA, SimpleToM, EmoBench, and MotiveBench, Social-R1 consistently outperforms strong reasoning LLMs; notably, Social-R1-4B surpasses LLaMA3-70B on all benchmarks despite the latter having more than ten times as many parameters. These results show that outcome-based RLVF substantially improves LLMs’ social reasoning while process-level thinking rewards provide additional gains, underscoring the importance of supervising the reasoning trajectory to foster human-like social intelligence in language models.