Reflective Recovery: A Self-Supervised Method for Reasoning by Learning from Mistakes
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arXiv:2609.19156v1 Announce Type: new Abstract: 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 S…
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- 2026-09-18 04:00 · arXiv cs.CL
Reflective Recovery: A Self-Supervised Method for Reasoning by Learning from Mistakes
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