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Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs research paper by Google, 2026

Google · Jun 30, 2026 · Foundation models · 1 citations · 28 upvotes · unverified

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What it shows

Reinforcement learning with metacognitive feedback and metacognitive data selection improve large language model calibration by enabling accurate self-assessment of performance and uncertainty.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona and 2 more
arXiv
2606.32032 · PDF
Venue
arXiv.org
Citations
1, 0 influential · Semantic Scholar
Upvotes
28 · Hugging Face
Code
github.com/yale-nlp/RLMF
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 1Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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