Instruction tuning LLM

Instruction tuning LLM is a post-training method that adapts large language models to follow human instructions and perform diverse tasks through supervised learning on instruction-response datasets. This process transforms base LLMs trained on next-token prediction into instruction-following assistants capable of understanding and executing complex commands. Instruction tuning employs datasets like Alpaca, Vicuna, or custom collections containing thousands of instruction-output pairs covering reasoning, coding, summarization, and creative tasks. The training methodology typically combines supervised fine-tuning with reinforcement learning from human feedback (RLHF) to align model behavior with human preferences and safety requirements. Key improvements include enhanced zero-shot task performance, better instruction comprehension, and reduced need for few-shot examples. For AI agents, instruction-tuned LLMs enable reliable task execution, natural language interfaces, and autonomous decision-making based on human directives, making them essential components for building responsive and controllable AI systems.