Reflective Recovery: A Self-Supervised Method for Reasoning by Learning from Mistakes
Data-driven fine-tuning is widely adopted to enhance reasoning in Large Language Models (LLMs) due to its simplicity and efficiency. However, mainstream imitation learning methods that rely exclusively on perfect reasoning trajectories suffer from a Scaling Collapse: when the problem set is limited, increasing positive examples fails to yield continuous improvement. However, during inference, an LLM can not guarantee that every intermediate step is correct and is therefore prone to errors. Once such errors arise, the LLM often struggles to recover and may be further misled by the accumulation of previous mistakes. To address this, we propose Reflective Recovery, a simple yet effective sel...
| Authors | Qirui Chen, Renjie Pi, Jiahui Gao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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