From Shortcuts to Reasoning: Robust Post-Training of Theory of Mind with Reinforcement Learning
arXiv:2606.09092v1 Announce Type: new Abstract: Theory of Mind (ToM) is a must-acquire skill for modern foundation model systems to operate effectively and safely in the real world. Recent works have explored honing ToM via post-training; however, we show that such progress is confounded by a pervasive "shortcut" issue: tasks can reach up to 99% accuracy by simply exploiting spurious causal correlations, leading to a false sense of ToM. Motivated by this, we first develop a framework to systematically examine ToM datasets for shortcuts and provide guidance for future development. We find that
The rapid advancement of foundation models necessitates more robust evaluation methods for critical capabilities like Theory of Mind, especially as these models are deployed in real-world scenarios.
Ensuring AI systems possess genuine Theory of Mind rather than relying on superficial correlations is crucial for their safe, effective, and ethical operation in complex human environments, impacting trust and reliability.
This research provides a framework to identify and mitigate 'shortcut' learning in AI ToM, pushing towards more genuinely intelligent and robust AI systems capable of understanding human intent.
- · AI safety researchers
- · Foundation model developers
- · AI ethics organizations
- · Developers relying on superficial ToM benchmarks
- · Systems with unverified ToM capabilities
Improved methods for evaluating and training AI with genuine Theory of Mind.
Accelerated development of more reliable and trustworthy AI agents capable of nuanced human interaction.
Increased public and regulatory confidence in advanced AI systems due to demonstrably robust cognitive abilities.
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Read at arXiv cs.LG