Instruction Tuning

Also known as: instruction fine-tuning, RLHF-SFT

Instruction tuning fine-tunes a pretrained language model on a dataset of instruction-response pairs so it learns to follow natural-language directions reliably. Without instruction tuning, a raw pretrained model predicts the next token but does not understand that it should answer questions, complete tasks, or follow constraints. Instruction tuning transforms the base model into an assistant that responds helpfully to diverse directives, and is the first stage of most production LLM alignment pipelines.