pith:OQMAWUXN
Training Language Models to Self-Correct via Reinforcement Learning
Multi-turn reinforcement learning trains language models to self-correct using only their own generated data.
arxiv:2409.12917 v2 · 2024-09-19 · cs.LG
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Claims
With Gemini 1.0 Pro and 1.5 Flash models, we find that SCoRe achieves state-of-the-art self-correction performance, improving the base models' self-correction by 15.6% and 9.1% respectively on MATH and HumanEval.
That training under the model's own distribution of self-generated correction traces combined with the described regularization will produce effective self-correction behavior at test time rather than fitting to high-reward but non-generalizable patterns.
SCoRe uses multi-turn online RL with regularization on self-generated traces to improve LLM self-correction, achieving 15.6% and 9.1% gains on MATH and HumanEval for Gemini models.
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| First computed | 2026-05-17T23:38:14.174994Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
74180b52ed219d10ea74f9fcc7f243bc28772308a634fc0eec79f2445249d030
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/OQMAWUXNEGORB2TU7H6MP4SDXQ \
| jq -c '.canonical_record' \
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Canonical record JSON
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