arXiv:2603.01195v2 Announce Type: replace-cross Abstract: The effectiveness of multimodal instruction tuning depends not only on dataset scale, but critically on whether training samples genuinely require visual reasoning. However, existing instruction datasets often contain a substantial portion of visually redundant samples (solvable from text alone), as well as multimodally misaligned supervision that can degrade learning. To address this, we propose VisNec (Visual Necessity Score), a principled data selection framework that measures the marginal contribution of visual input during instruct
The proliferation of multimodal AI models has outpaced rigorous data curation methodologies, leading to a recognized need for more efficient and effective training strategies.
This development addresses a critical bottleneck in multimodal AI training by improving data efficiency, which directly impacts model performance, development costs, and the viability of complex AI applications.
The explicit measurement of visual necessity in multimodal training data will likely lead to more robust and less resource-intensive model development, focusing efforts on truly impactful data.
- · AI model developers
- · Multimodal AI research institutions
- · Companies with limited compute resources
- · Developers relying solely on brute-force data scaling
- · Generative AI models with poor visual reasoning
Multimodal AI models will exhibit improved reasoning capabilities and reduced training costs.
The development cycle for complex AI applications requiring visual understanding will accelerate.
More sophisticated and reliable AI agents capable of nuanced real-world interaction will become feasible due to enhanced multimodal understanding.
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Read at arXiv cs.AI