AIBullisharXiv – CS AI · 9h ago7/10
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Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models
Researchers propose a new training paradigm called ReVision that addresses the 'modality gap'—a geometric misalignment between visual and text embeddings in multimodal AI models. By introducing ReAlign, a training-free alignment strategy that leverages unpaired data statistics, the framework enables efficient scaling of multimodal large language models without requiring expensive paired image-text datasets.