Instruction Tuning vs Fine Tuning

Instruction tuning vs fine tuning represents two distinct approaches to adapting pre-trained language models, with instruction tuning focusing on teaching models to follow diverse natural language instructions while traditional fine tuning optimizes performance on specific tasks. Instruction tuning employs multi-task datasets containing instruction-response pairs across various domains, enabling models to generalize to new instruction types and perform zero-shot task completion. Traditional fine tuning adapts models using task-specific datasets with examples of input-output pairs for particular objectives like sentiment analysis or named entity recognition. Instruction tuning prioritizes instruction-following capabilities and broad generalization, while fine tuning maximizes performance on targeted tasks. Instruction tuning typically uses conversational formats and diverse prompts, whereas fine tuning employs structured task-specific data. For AI agents, instruction tuning creates more versatile systems capable of interpreting and executing varied commands, while traditional fine tuning optimizes specialized capabilities.