Supervised Fine-Tuning

The initial stage of adapting a pre-trained language model to follow instructions, where the model is trained on a curated dataset of high-quality prompt-response pairs using standard supervised learning. SFT is typically the first step in turning a raw pre-trained model into a useful assistant, usually followed by preference optimization and reinforcement learning from human feedback.