Han Lin

Efficient Multimodal Instruction Tuning Through Iterative Data Selection and Augmentation Under Fixed Data Budgets

Efficient multimodal instruction tuning requires reducing both training data volume and computational cost without substantially sacrificing downstream performance. Existing data selection methods can remove redundancy but cannot repair imperfect supervision or supplement under-represented capabilities. We propose SANe...