MAGIC: Multimodal Alignment & Grounding-aware Instruction Coreset for Vision-Language Models
arXiv:2605.26004v1 Announce Type: cross Abstract: Instruction tuning of large vision-language models (LVLMs) increasingly depends on massive multimodal corpora, yet these datasets contain samples with substantial redundancy, low visual dependency, and highly imbalanced coverage of multimodal reasoning behaviors. As a result, uniform subsampling or naive score-based selection often yields suboptimal training subsets. We introduce MAGIC, a training-free, forward-only coreset selection method designed to construct compact yet behaviorally faithful subsets for multimodal instruction tuning. MAGIC
The proliferation of increasingly massive multimodal models necessitates more efficient and effective data selection methods to sustain progress and manage computational costs.
Improving the efficiency and quality of instruction tuning for large vision-language models directly impacts the development speed and capabilities of advanced AI, potentially democratizing access to powerful models.
The introduction of methods like MAGIC simplifies the creation of high-quality training datasets for multimodal AI, making model development more resource-efficient and accessible.
- · AI researchers and developers
- · Companies with limited compute resources
- · Open-source AI initiatives
- · Vision-Language Model applications
- · Inefficient AI training methodologies
- · Organizations relying solely on brute-force data scaling
More compact and effective training datasets for multimodal instruction tuning become widely available.
This leads to faster iteration cycles for vision-language models and potentially more specialized, high-performing AI agents.
Improved efficiency in AI development could accelerate the deployment of sophisticated AI agents across various sectors, impacting white-collar workflows.
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Read at arXiv cs.CL