Instruction Tuning

Instruction Tuning is a form of supervised fine-tuning that trains a language model to follow natural language instructions across a diverse range of tasks. Rather than fine-tuning on a single task, instruction tuning exposes the model to many tasks — each phrased as a natural language instruction (e.g., 'Summarize the following article') with the expected output. This teaches the model the general skill of instruction-following rather than memorizing task-specific patterns.

The key to effective instruction tuning is task diversity. By training on thousands of tasks spanning summarization, translation, question answering, code generation, and more — all expressed as instructions — the model learns to generalize to unseen tasks at inference time. This is what enables zero-shot and few-shot task performance: a model that has been instruction-tuned can follow a novel instruction it has never explicitly been trained on, because it has learned the meta-skill of mapping instructions to appropriate responses.

Instruction tuning can use human-written demonstrations or synthetically generated instruction-response pairs (as in the Self-Instruct approach). It is closely related to and often used interchangeably with supervised fine-tuning, though instruction tuning specifically emphasizes multi-task diversity and generalization to novel instructions. It is a prerequisite for alignment methods like RLHF, which refine the model's instruction-following behavior based on human preferences.