Metacognitive Reliability Supervision Teaches LLM Agents to Search Only When Needed
_reachsumit · x · 2026-11-02
An arXiv paper introduces a metacognitive framework that trains LLM agents to decide when to search versus proceed with current knowledge.
- Problem: supervision based on final-answer correctness or trajectory-level feedback only indirectly indicates how reliable the current decision state was, since trajectory performance is jointly shaped by stochastic generation, retrieval quality, and downstream reasoning
- Method: estimate the reliability of local conclusions via two scores — current-state reliability (can the model proceed without search?) and search-action score (how much retrieved evidence improves reliability)
- Together these form a reliability-aware process reward for group-relative RL training, encouraging search only when external information is needed and favoring searches that actually contribute to the answer
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