2 papers
cs.CV2026
Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning
Mingkang Dong, Hongyi Cai, Xiwen Lei +3
Multimodal instruction tuning is the de facto recipe for adapting vision language models (VLMs), yet instruction data are highly redundant, making data selection critical for train…
cs.CL2026
Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning
Hongyi Cai, Jie Li, Mohammad Mahdinur Rahman +1
The effectiveness of instruction fine-tuning for Large Language Models is fundamentally constrained by the quality and efficiency of training datasets. This work introduces Low-Con…