OnceSelect: Reusable Data Selection for Efficient Multimodal Instruction Tuning
Multimodal instruction tuning is widely used to adapt multimodal large language models (MLLMs), yet the large-scale image-text datasets it relies on are often highly redundant. Existing data selection methods are commonly tied to specific datasets, target models, or training states, requiring retraining or recomputation when transferred to new settings. We ask whether a selection signal can instead be learned once and reused across datasets and models. We propose OnceSelect, a reusable data selector trained once and directly applied to unseen datasets. OnceSelect encodes image-instruction pairs in a frozen joint multimodal space, clusters them into pseudo-labels capturing coarse semantic ...
| Authors | Mingkang Dong, Muxin Pu, Hongyi Cai et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 May 2026 |
| Last updated at source | 4 October 2026 |
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