supervised fine-tuning

Supervised fine-tuning (SFT) adapts a pretrained model on labeled instruction–response pairs so it follows directives and output formats, and typically precedes preference tuning like RLHF. SFT improves instruction adherence and formatting but does not guarantee factual accuracy — a well-tuned model can still hallucinate.