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Showing papers from Meta FAIR & Université Paris-Dauphine PSL Show all papers

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NeurIPS 2026Meta AIMeta FAIR & Université ParisMeta / Ecole Normale SupérieureInriaFacebook AI ResearchRL for LLMs

Reinforcement Learning for Code Optimization

Reinforcement learning for code optimization fails due to noisy, sparse execution-time rewards, so a calibrated three-stage pipeline improves strict pass rates by up to 125% while preserving correctness.

Pierre Chambon, Kunhao Zheng, Juliette Decugis, Benoît Sagot and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 13 on Hugging Face

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AI panel: 19 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 5/5
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NeurIPS 2026Meta FAIR & Université ParisMeta / Ecole Normale SupérieureMeta AIMeta AIMeta / FAIRRL for LLMs

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

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17/20 AI panelreviewers recommend it

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