Instruction Following

Instruction-following research asks whether language models and agents can carry out what users request, including exact formats, multiple constraints, changing requirements, and rules that override other instructions. Researchers test these abilities with automatically checked benchmarks and realistic settings such as long conversations, tool use, coding, speech, and embodied tasks. To improve performance, they use supervised fine-tuning, preference learning and reinforcement learning with verifiable or rubric-based rewards, as well as prompt compilation, model steering, and inference-time checks. Models can handle many simple instructions, but reliability often falls when constraints interact or accumulate, and improving compliance without harming reasoning, task quality, or general capabilities remains a central challenge.