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Reasoning for control can be transformed into an adaptive, iterative process that leverages a latent memory structure, yielding superior performance in complex tasks.
Reusing training data during inference can boost imitation learning performance by up to 46%, reshaping how we approach generalization in AI systems.
End-to-end training of Vision-Language-Action models just got a whole lot easier: VLA Foundry unifies LLM, VLM, and VLA training in a single open-source framework.
