Small LLMs Do Not Learn a Generalizable Theory of Mind via Reinforcement Learning

• URL: http://arxiv.org/abs/2507.15788v1
• Date: Mon, 21 Jul 2025 16:47:59 GMT
• Title: Small LLMs Do Not Learn a Generalizable Theory of Mind via Reinforcement Learning
• Authors: Sneheel Sarangi, Hanan Salam,
• Abstract summary: Small language models (LLMs) struggle to develop a generic Theory of Mind (ToM) capability.<n> prolonged RL training leads to models hacking'' the statistical patterns of the training datasets.<n>This suggests the learned behavior is a form of narrow overfitting rather than the acquisition of a true, abstract ToM capability.
• Score: 1.6114012813668932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in large language models (LLMs) have demonstrated emergent capabilities in complex reasoning, largely spurred by rule-based Reinforcement Learning (RL) techniques applied during the post-training. This has raised the question of whether similar methods can instill more nuanced, human-like social intelligence, such as a Theory of Mind (ToM), in LLMs. This paper investigates whether small-scale LLMs can acquire a robust and generalizable ToM capability through RL with verifiable rewards (RLVR). We conduct a systematic evaluation by training models on various combinations of prominent ToM datasets (HiToM, ExploreToM, FANToM) and testing for generalization on held-out datasets (e.g., OpenToM). Our findings indicate that small LLMs struggle to develop a generic ToM capability. While performance on in-distribution tasks improves, this capability fails to transfer to unseen ToM tasks with different characteristics. Furthermore, we demonstrate that prolonged RL training leads to models ``hacking'' the statistical patterns of the training datasets, resulting in significant performance gains on in-domain data but no change, or degradation of performance on out-of-distribution tasks. This suggests the learned behavior is a form of narrow overfitting rather than the acquisition of a true, abstract ToM capability.

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