New Training-Free Method Improves Vision-Language Model Efficiency by Selecting High-Quality Data
Researchers have proposed CVS, a training-free data selection method for vision-language large models that identifies samples requiring genuine cross-modal reasoning rather than linguistic shortcuts. The method works by measuring how much a question changes a model's assessment of answer validity when paired with an image, using a frozen model as an evaluator. CVS outperforms full-data training using only 10–15% of available data while significantly reducing computational costs compared to existing approaches.
A preprint posted to arXiv introduces CVS (Conditional Validity Scoring), a training-free approach to selecting high-quality data for visual instruction tuning of vision-language large models (VLLMs). The core insight is that genuinely multimodal samples should cause a model's assessment of answer validity to shift substantially when the question is introduced alongside an image, as opposed to samples solvable through linguistic patterns or common-sense shortcuts alone. By leveraging a frozen VLLM as an evaluator and measuring this discrepancy, CVS filters out semantically conflicting or low-value samples without requiring expensive proxy model training. Experiments on the Vision-Flan and The Cauldron datasets demonstrate that CVS using only 10% and 15% of training data outperforms full-data training by 3.5% and 4.8%, respectively. The method also proves robust on the highly heterogeneous Cauldron dataset and reduces computational overhead by 17.3% and 44.4% compared to competing methods COINCIDE and XMAS.
What's missing
The study is a preprint and has not yet undergone peer review. The experiments are limited to two datasets (Vision-Flan and The Cauldron), and it is unclear how CVS generalizes to other multimodal benchmarks or model architectures. The paper does not report results across a wide range of model sizes, leaving scalability an open question.
What different sources said
- arXiv cs.AICenter
Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT
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