Supervised Fine-Tuning for Reasoning Large Language Models: A Survey

The paper surveys recent progress in supervised fine‑tuning (SFT) for large language models that excel at reasoning tasks. It reviews methodological advances, grouping them into data‑centric and algorithm‑centric approaches, and reorganizes analytical studies by data characteristics, optimization dynamics, and mechanistic insights. The survey also identifies current research focuses, remaining bottlenecks, and suggests future directions for reasoning SFT.

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• llms
• fine-tuning