DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes
Reinforcement learning has become a central paradigm for advancing reasoning in large language models, yet most existing methods still depend on stronger teacher models or heavily curated difficult datasets, limiting scalable capability improvement. In this paper, we introduce DenoiseRL, a reinforcement learning framework that substitutes external supervision with recovery-oriented optimization over failures from weak models. Instead of relying on stronger supervision or carefully engineered data, DenoiseRL learns directly from noisy reasoning prefixes by converting them into opportunities for improvement, while exercising fine-grained control over the noise intensity, making training mor...
| Authors | Caijun Xu, Changyi Xiao, Zhongyuan Peng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 May 2026 |
| Last updated at source | 30 July 2026 |
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