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Forget end-to-end fine-tuning: $M^2$-VLA unlocks the power of generalized VLMs for robotic manipulation by intelligently mixing layers and incorporating meta-skills.
Forget fixed pipelines: training an agent to *learn* when and how to search for knowledge dramatically improves performance on knowledge-based visual question answering.
