Instruction Tuning
A fine-tuning approach where a model is trained on many instruction-and-response examples to improve its ability to follow user requests.
Instruction tuning is the process of training a model on a large dataset of prompts and ideal responses so it becomes better at following human instructions. It is one of the key steps that turns a raw base model into a useful assistant.
Before instruction tuning, a model may be good at continuing text but poor at directly answering questions or following formatting requests. After instruction tuning, it becomes much more responsive to user intent.
Why Instruction Tuning Matters
- Better usability — models become easier to interact with directly
- Stronger formatting control — more likely to follow requested structure
- Foundation for assistants — often precedes alignment steps like RLHF
- Broader generalization — works across many task types
Instruction tuning is usually done after pretraining and before or alongside stronger alignment methods such as RLHF. It is a major milestone in the development of chat-style AI systems.