Multimodal Instruction Tuning

Multimodal instruction tuning teaches models to answer questions and perform tasks by pairing instructions with inputs such as images, video, audio, or scientific signals. Common approaches use supervised fine-tuning on curated or synthetically generated examples, sometimes followed by preference optimization or reinforcement learning with verifiable rewards. Researchers also select high-value training examples and use parameter-efficient adapters or expert routing to reduce costs and support continual adaptation. A central challenge is getting models to ground answers in the relevant modality while preserving existing capabilities and generalizing across tasks, domains, and complex instruction constraints.