Instruction Tuning

Instruction tuning adapts a pretrained model using examples that pair an instruction with an appropriate response. The training set may cover summarization, question answering, extraction, writing, refusal behavior, and many other tasks, helping the model respond more reliably to natural-language requests. Unlike learning one narrow capability, a diverse instruction collection aims to improve general task following. Quality matters more than simple volume because contradictory, unsafe, or poorly written examples can teach undesirable patterns. Evaluation should test unfamiliar instructions, factuality, formatting, and safety boundaries. Instruction tuning is often combined with preference training or other alignment methods, but it cannot guarantee that every future request will be understood correctly.