arXiv:2603.09715v2 Announce Type: replace 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
This research emerges as the field of large multimodal models matures, highlighting the critical need for efficient and effective training data selection to overcome current limitations in genuine cross-modal reasoning.
Improved data selection for VLLMs reduces training costs and enhances the models' ability to perform true vision-language joint reasoning, moving beyond linguistic shortcuts.
The proposed training-free method offers a more accessible and efficient way to curate training datasets, potentially accelerating the development of more robust and intelligent vision-language AI.
- · AI researchers and developers
- · Companies developing VLLMs
- · Sectors reliant on multimodal AI applications
- · Prior methods requiring costly proxy model training
- · Developers stuck with inefficient data curation pipelines
More efficient and higher-performing vision-language large models become feasible.
Reduced barriers to entry for developing advanced multimodal AI, potentially democratizing access to powerful AI tools.
Accelerated innovation in autonomous systems and agents that require genuine multimodal understanding, leading to new categories of AI applications.
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