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Medical VLMs can achieve state-of-the-art visual reasoning without any human annotations, thanks to a novel reinforcement learning framework that leverages model uncertainty and rollout agreement.
SubFLOT tackles federated learning's heterogeneity problem by cleverly using optimal transport to create personalized submodels on the server, sidestepping the computational burden of client-side pruning.
