Computer Science > Artificial Intelligence
arXiv:2603.09715 (cs)
[Submitted on 10 Mar 2026]
Title:Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT
View a PDF of the paper titled Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT, by Peng Sun and 5 other authors
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Abstract:Visual instruction tuning is crucial for improving vision-language large models (VLLMs). However, many samples can be solved via linguistic patterns or common-sense shortcuts, without genuine cross-modal reasoning, limiting the effectiveness of multimodal learning. Prior data selection methods often rely on costly proxy model training and focus on difficulty or diversity, failing to capture a sample's true contribution to vision-language joint reasoning. In this paper, we propose CVS, a training-free data selection method based on the insight that, for high-quality multimodal samples, introducing the question should substantially alter the model's assessment of answer validity given an image. CVS leverages a frozen VLLM as an evaluator and measures the discrepancy in answer validity with and without conditioning on the question, enabling the identification of samples that require vision-language joint reasoning while filtering semantic-conflict noise. Experiments on Vision-Flan and The Cauldron show that CVS achieves solid performance across datasets. On Vision-Flan, CVS outperforms full-data training by 3.5% and 4.8% using only 10% and 15% of the data, respectively, and remains robust on the highly heterogeneous Cauldron dataset. Moreover, CVS reduces computational cost by 17.3% and 44.4% compared to COINCIDE and XMAS.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09715 [cs.AI] |
| (or arXiv:2603.09715v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09715
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View a PDF of the paper titled Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT, by Peng Sun and 5 other authors
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