Instruction Fine-Tuning

Instruction fine-tuning is a supervised learning technique that trains pre-trained language models to better follow human instructions and complete specific tasks through natural language prompts. This process uses curated datasets containing instruction-response pairs, where models learn to map diverse instruction formats to appropriate outputs. Unlike traditional fine-tuning on single tasks, instruction fine-tuning employs multi-task datasets like InstructGPT, Alpaca, or FLAN collections that cover reasoning, summarization, question-answering, and creative tasks. The training objective typically uses supervised fine-tuning followed by reinforcement learning from human feedback (RLHF) to align outputs with human preferences. This approach enables zero-shot generalization to unseen instruction types and improves model helpfulness, harmlessness, and honesty. For AI agents, instruction fine-tuning is essential for creating systems that reliably interpret and execute complex user commands, enabling autonomous task completion, workflow automation, and natural human-AI collaboration.