Good Papers

Showing papers from Meta AI (FAIR) Show all papers

91%Must read
?Must readVote to see the score

Extrapolative Weight Averaging Reveals Correctness–Efficiency Frontiers in Code RL

Nested unit-test coverage in code RL reveals a correctness, efficiency frontier that extrapolative weight averaging extends, enabling complementary checkpoints that improve pass@250 by 3.3%.

Kunhao Zheng, Juliette Decugis, Pierre Chambon, Jonas Gehring and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
17/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 17 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 4/5