Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs

Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one’s own cognitive processes. Yet LLMs exhibit systemic deficiencies in key metacognitive faculties: they hallucinate with high confidence, fail to recognize knowledge boundaries, and misrepresent their internal uncertainty— undermining trustworthiness and reliability. Since monitoring task performance and adapting behavior accordingly are central to metacognition, we posit that models capable of accurately judging their own performance are better positioned to improve it. We operationalize this idea via two novel mechanisms: reinforcement learning with metacognitive feedback (RLMF), a paradigm to refine completion rankings during preference optimization based on the quality of a model’s selfjudgments of performance, and metacognitive data selection, which uses similar self-judgments to identify high-value training examples, outperforming naive active learning. We apply these innovations to the problem of faithful calibration (FC), a task that is itself fundamentally metacognitive: the goal is to align expressed with intrinsic uncertainty, difficult even for frontier LLMs.

Introduction. Metacognition is a foundational component of intelligence that refers to the ability to monitor, assess, and regulate one’s own cognitive processes [23]. It is critical to effective learning, decision-making, and communication and has become increasingly recognized as a cornerstone of capable, transparent AI systems [88]. Despite this, LLMs continue to exhibit key metacognitive deficiencies, including failure to recognize knowledge boundaries [89], tendency toward high-confidence hallucinations [83], and systematic misrepresentation of their internal uncertainty [113, 62]. This lack of robust metacognitive faculties undermines trustworthiness and reliability, particularly as models are deployed in downstream advisory roles across high-stakes settings such as scientific discovery [85, 122], medical diagnosis [46, 130], and legal consulting [15, 55].

Discussion / Conclusion. We introduced RLMF, a novel paradigm to refine completion rankings during preference optimization by leveraging a model’s own implicit judgments of performance, alongside metacognitive data selection, which uses similar self-judgments to identify more effective training data than simple active learning. We applied these contributions to build the first end-to-end framework for holistic faithful calibration (FC) of LLMs, presenting a two-stage decoupled approach to robustly align models’ numerically and linguistically expressed uncertainty with their intrinsic confidence. Comprehensive experiments showed this framework achieves strong and generalizable FC across diverse models and tasks, outperforming the prior state-of-the-art while preserving task accuracy and factual calibration. It also enables LLMs to improve at self-assessment of performance, emit highly faithful self-reported confidence scores, and modulate linguistic uncertainty in a naturalistic, context-appropriate fashion. As part of these evaluations, we introduced cMFG*, a new metric that improves upon its predecessor by removing estimation bias for models whose intrinsic confidence occupies a limited range.