The belief-action gap in language model agents
We study how state beliefs, action values, and action preferences interact as LM agents reason and commit to decisions in text-rendered grid worlds.
ProofPaper ↗
Key points
- Language model (LM) agents can fail for different reasons, requiring different explanations and interventions.
- We estimate task-relevant beliefs using report-based and representation-based probes, and action values using inverse reinforcement learning.
- We find that task-relevant state and value information is often represented more reliably than it is reflected in behaviour.
- Tracking action preferences throughout reasoning further shows that decisions sharpen around a commitment point and reported belief uncertainty decreases near commitment, although individual belief changes do not consistently explain shifts in action preference.
Sources (1)
- [1]The belief-action gap in language model agentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 10:04 PM
We study how state beliefs, action values, and action preferences interact as LM agents reason and commit to decisions in text-rendered grid worlds.
Language model (LM) agents can fail for different reasons, requiring different explanations and interventions.
Extractive summary: sentences quoted from the sources.
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