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Robot foundation models can now learn new tasks rapidly and retain old ones, slashing real-world training time by up to 68.5% without significant forgetting.
A scalar adjoint method slashes the computational cost of fine-tuning flow policies in RL, achieving state-of-the-art results on complex manipulation tasks.
