Chain-of-Thought SFT: Fine-Tuning a Thinking Model Locally
Researchers have detailed a method for locally fine-tuning large language models using a Chain-of-Thought (CoT) approach. This technique, termed CoT SFT, aims to improve the model's reasoning capabilities by training it to generate intermediate thinking steps. The process leverages LoRA (Low-Rank Adaptation) for efficient fine-tuning, demonstrating its application with models like Qwen3 and Sky-T1. AI
IMPACT This method could enable more efficient and effective local fine-tuning of LLMs for complex reasoning tasks.