Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Score
59Why selected
Novel methodology with thorough experiments and significant results, addressing core reliability issues in LLMs with broad applicability.
AI commentary
「This work pioneers using metacognitive ability as an RL signal, offering a new paradigm for improving LLM reliability and alignment.」
Quick overview
This paper proposes Reinforcement Learning with Metacognitive Feedback (RLMF), a paradigm that refines completion rankings based on the quality of model self-judgments, along with metacognitive data selection. Applied to faithful calibration, it aligns expressed with intrinsic uncertainty. Extensive experiments show RLMF achieves state-of-the-art calibration while preserving accuracy, surpassing standard RL by up to 63% and enhancing models' ability to assess and express capability limits.
- Proposes RLMF that uses the quality of model self-judgments as reward signal
- Introduces metacognitive data selection to identify high-value examples via self-judgments
- Achieves state-of-the-art faithful calibration, outperforming standard RL by up to 63%
- Preserves task accuracy while enhancing models' uncertainty expression
Full text
Computer Science > Computation and Language
arXiv:2606.32032v1 (cs) [Submitted on 30 Jun 2026]Title:Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Authors:Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona, Idan Szpektor, Arman Cohan View a PDF of the paper titled Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs, by Gabrielle Kaili-May Liu and 4 other authors View PDF HTML (experimental)Abstract: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 self-judgments 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. We adopt a two-stage, decoupled approach, first using these methods to calibrate the faithfulness of models' self-reported confidence scores, then mapping to natural, context-adaptable linguistic uncertainty via targeted output editing. Extensive experiments show RLMF achieves generalizable, state-of-the-art FC on diverse tasks while preserving accuracy. Further, RLMF surpasses standard RL by up to 63% while enhancing models' ability to assess and express their own capability limits. This positions RLMF as a promising paradigm to enhance LLM metacognition toward improved abilities and alignment, and suggests metacognitive performance as an effective RL signal to overcome limits of prior intrinsic feedback methods.
| Comments: | Code: this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.32032 [cs.CL] |
| (or arXiv:2606.32032v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.32032 Focus to learn more arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Gabrielle Liu [view email][v1] Tue, 30 Jun 2026 17:56:01 UTC (3,482 KB)
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