Instruction tuning is a supervised learning, fine-tuning technique used to train large language models (LLMs) to follow user instructions rather than simply predict the next word in a sequence. During pretraining, LLMs learn linguistic patterns from vast amounts of text, but this does not automatically make them useful for conversations or task completion. Instruction tuning bridges that gap by training the model on labeled instruction-response pairs that demonstrate what a helpful, relevant reply looks like.
The process works by adjusting model parameters using gradient descent and backpropagation to bring the model's outputs closer to the example responses in the instruction dataset. Compared to pretraining from scratch, instruction tuning requires far less data and computational resources, making it a practical way to improve a model's behavior without rebuilding it entirely.
Instruction tuning is closely related to reinforcement learning from human feedback (RLHF), which takes alignment further by incorporating human preference ratings to guide model behavior beyond what labeled examples alone can achieve.
Including chain-of-thought (COT) examples in instruction datasets has also been shown to improve a model's ability to reason through problems step by step, rather than producing answers that are linguistically plausible but logically unsound.