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China Agricultural University
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VLAs learn to predict task success even when trained only with imitation learning, opening the door to improved performance without expensive reward engineering.
Mirror Descent, a workhorse of large-scale optimization, now has a Riemannian generalization with convergence guarantees, opening doors to efficient optimization on curved spaces.
