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martin-marek/score-centering

📄Score Centering Stabilizes Off-policy Reinforcement Learning

#LLM training
Visit GitHub
Python
182
14
2 contributors
MIT License
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How fast martin-marek/score-centering 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 17 – Oct 20, 2026

Stars +170 in 34 days

0102030
30

Forks +14 in 34 days

0123
3

Merged PRs 1 in 34 days

01
1

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

Not in the Trendshift top 25 in this period

Sep 17Sep 22Sep 27Oct 2Oct 7Oct 12Oct 17
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
Thu, Sep 17, 2026+0+00
Fri, Sep 18, 2026+11+01
Sat, Sep 19, 2026+23+30
Sun, Sep 20, 2026+10+30
Mon, Sep 21, 2026+19+20
Tue, Sep 22, 2026+30+00
Wed, Sep 23, 2026+14+00
Thu, Sep 24, 2026+5+10
Fri, Sep 25, 2026+1+00
Sat, Sep 26, 2026+1+00
Sun, Sep 27, 2026+3+00
Mon, Sep 28, 2026+7+00
Tue, Sep 29, 2026+5+00
Wed, Sep 30, 2026+2+10
Thu, Oct 1, 2026+2+00
Fri, Oct 2, 2026+2+00
Sat, Oct 3, 2026+1+00
Sun, Oct 4, 2026+2+00
Mon, Oct 5, 2026+1+00
Tue, Oct 6, 2026+1+00
Wed, Oct 7, 2026+3+00
Thu, Oct 8, 2026+1+10
Fri, Oct 9, 2026+4+00
Sat, Oct 10, 2026+3+10
Sun, Oct 11, 2026+1+00
Mon, Oct 12, 2026+3+00
Tue, Oct 13, 2026+1+00
Wed, Oct 14, 2026+3+10
Thu, Oct 15, 2026+1+00
Fri, Oct 16, 2026+3+00
Sat, Oct 17, 2026+0+00
Sun, Oct 18, 2026+1+00
Mon, Oct 19, 2026+3+10
Tue, Oct 20, 2026+3+00
Total+170+141
  • 1#1 on the list
  • 7Rank that day
  • Not trending

Social mentions

Recent discussions about this repository across the web
  1. Sep 251 post
    • 在量化(INT8/INT4/FP4)场景下,SC单独或组合即显著领先,严重不匹配时唯一稳定收敛(30B模型达30%准确率,其余多低于5%);在高staleness(每64步同步)下,SC+TIS/MIS最优。

      Read full on X, posted by @CyberSoma1024

      @CyberSoma1024 · x.com

  2. 7 days earlier
  3. Sep 182 posts
    • RL with LLMs is very unstable when training and sampling policies differ. Standard fixes (matching numerics, importance sampling) work around the problem. We find the root cause of this instability…

      Read full on X, posted by @m_ryabinin

      @m_ryabinin · x.com

    • How expensive is Score Centering? We implement it using a top-k approximation applied on top of arbitrary importance ratios, expressed as a scalar loss. Assuming we're already transferring top-k…

      Read full on X, posted by @mrtnm

      @mrtnm · x.com

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