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 more scalable and effective. This yields a richer and more diverse learning signal, improving exploration efficiency by leveraging imperfect model behavior. Empirically, DenoiseRL consistently outperforms strong on-policy RL baselines across competitive mathematical reasoning tasks and interactive decision-making tasks, while promoting stronger self-corrective behavior as training difficulty increases, highlighting an effective and scalable pathway for improving agentic reasoning capabilities of large language models.

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