SFT

Also known as: Supervised Fine-Tuning

Supervised fine-tuning trains a language model on a curated dataset of prompt-completion pairs using standard cross-entropy loss, where the model learns to predict the correct completion token by token. SFT is typically the first adaptation stage after pretraining and is used for instruction following, task specialization, and style transfer. The quality and format of the SFT dataset directly shape the model's output style and capability ceiling before any preference alignment stage.