pith:23J2LGEK
ICRL: Learning to Internalize Self-Critique with Reinforcement Learning
ICRL jointly trains a solver and critic from one backbone so critique gains become part of unassisted performance.
arxiv:2605.15224 v1 · 2026-05-13 · cs.AI · cs.MA
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ICRL jointly trains a solver and a critic from a shared backbone to convert critique-induced success into unassisted solver ability, with results showing average gains of 6.4 points over GRPO on agentic tasks and 7.0 points on mathematical reasoning using Qwen3-4B and Qwen3-8B backbones.
The distribution-calibration re-weighting ratio successfully selects critique-guided improvements that remain compatible with the solver's own prompt distribution, without introducing bias or reducing performance on critique-free queries.
ICRL uses joint RL training of solver and critic with distribution-calibration re-weighting and role-wise advantage estimation to internalize critique into unassisted LLM performance, yielding 6.4-point gains on agentic tasks and 7.0 on math reasoning with Qwen3 models.
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| First computed | 2026-05-20T00:00:47.193945Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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