LLM Instruction Tuning

LLM instruction tuning is a specialized training methodology that adapts large language models to follow human instructions and complete 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 natural language commands. The methodology typically involves supervised fine-tuning on curated instruction datasets like Alpaca, Dolly, or OpenAssistant, followed by reinforcement learning from human feedback (RLHF) to align outputs with human preferences. LLM instruction tuning employs multi-task learning where models learn to handle reasoning, summarization, coding, creative writing, and question-answering through diverse instruction formats. This approach enables zero-shot generalization to unseen instruction types while maintaining the broad knowledge acquired during pre-training. For AI agents, LLM instruction tuning creates reliable systems that interpret user commands, execute multi-step tasks, and provide helpful responses.