Supervised Fine-Tuning (SFT)

In depth

Fine-tuning a pretrained model on curated prompt and response pairs so it learns to imitate the demonstrated answers. It uses the same next-token loss as pretraining, applied to high-quality examples. SFT is usually the first post-training step: OpenAI's InstructGPT fine-tuned GPT-3 on labeler-written demonstrations before applying RLHF. It is cheap and predictable for teaching format, style, and tool-call syntax, but it can only copy behavior already in the data, which is why reinforcement learning usually follows it.

Primary source: arxiv.org