Instruction Tuning

Instruction tuning studies how to turn pretrained models into systems that respond usefully to instructions, often through supervised fine-tuning on instruction-response pairs and sometimes preference optimization or reinforcement learning. Research develops ways to select, balance, and generate training data, including methods for multilingual, domain-specific, and multimodal tasks. Evaluations use instruction-following tests such as IFEval alongside reasoning, coding, dialogue, and specialized-domain benchmarks. A central challenge is that gains can depend on task and format: tuning may improve instruction compliance while harming other capabilities, and reliable generalization beyond training examples remains difficult.