Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning 事件

Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning arXiv:2605.26761v1 Announce Type: new Abstract: 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 training efficiency. Existing methods derive selection signals from a specific model or dataset, so whenever the target model or candidate pool changes, the criteria must be recomputed f