RLMF
LLMs routinely hallucinate with high confidence, miss their own knowledge boundaries, and misreport uncertainty, and most fixes bolt calibration on from the outside. RLMF, a Google and Yale collaboration, instead turns the model’s own metacognition into the training signal. ---
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Metacognition as the reward: The method refines completion rankings during preference optimization based on the quality of the model’s self-judgments, using how well a model assesses its own performance as an internal feedback signal.
A decoupled, two-stage recipe: It first calibrates the faithfulness of self-reported confidence scores, then maps those scores to natural, context-adaptable linguistic uncertainty through targeted output editing.
Better calibration without losing accuracy: RLMF reaches state-of-the-art faithful calibration across diverse tasks, surpasses standard RL by a wide margin, and sharpens the model’s ability to express its own capability limits.
Why it matters: Grounding calibration in the model’s own metacognition rather than external heuristics offers a more general path to trustworthy uncertainty, which is foundational for agents that must know when not to act.