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yale-nlp/RLMF

Code for COLM 2026 Paper "Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs"

#LLM training
Visit GitHub
Python
36
7
1 contributors
MIT License
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Momentum

How fast yale-nlp/RLMF is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Sep 27 – Nov 14, 2026

Stars +3 in 49 days

01
1

Forks +0 in 49 days

01

Merged PRs 0 in 49 days

01

Trendshift Trendingwhere the repository ranked each day it made the top 25

Not in the Trendshift top 25 in this period

Sep 27Oct 4Oct 11Oct 18Oct 25Nov 1Nov 8
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRs
Sun, Sep 27, 2026+1+00
Mon, Sep 28, 2026+0+00
Tue, Sep 29, 2026+0+00
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Thu, Oct 1, 2026+0+00
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Thu, Oct 8, 2026+0+00
Fri, Oct 9, 2026+0+00
Sat, Oct 10, 2026+0+00
Sun, Oct 11, 2026+0+00
Mon, Oct 12, 2026+1+00
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Mon, Oct 26, 2026+0+00
Tue, Oct 27, 2026+0+00
Wed, Oct 28, 2026+0+00
Thu, Oct 29, 2026+0+00
Fri, Oct 30, 2026+0+00
Sat, Oct 31, 2026+0+00
Sun, Nov 1, 2026+0+00
Mon, Nov 2, 2026+0+00
Tue, Nov 3, 2026+0+00
Wed, Nov 4, 2026+0+00
Thu, Nov 5, 2026+0+00
Fri, Nov 6, 2026+1+00
Sat, Nov 7, 2026+0+00
Sun, Nov 8, 2026+0+00
Mon, Nov 9, 2026+0+00
Tue, Nov 10, 2026+0+00
Wed, Nov 11, 2026+0+00
Thu, Nov 12, 2026+0+00
Fri, Nov 13, 2026+0+00
Sat, Nov 14, 2026+0+00
Total+3+00
  • 1#1 on the list
  • 7Rank that day
  • Not trending

Social mentions

Recent discussions about this repository across the web
  1. Jul 21 post
    • what i think is underappreciated here: metacognitive performance as an RL signal is a genuinely new axis. most intrinsic feedback methods use raw confidence or entropy as rewards. this goes one level…

      Read full on X, posted by @romir_jain

      @romir_jain · x.com

  2. Jul 11 post
    • (18/n) Excitingly, these results suggest use of metacognitive signals during RL as a promising paradigm for stronger, more stable post-training that simultaneously sharpens LLMs’ metacognitive…

      Read full on X, posted by @pybeebee

      @pybeebee · x.com

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