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Robots can now learn complex manipulation skills from human videos by focusing on transferable causal dependencies, bypassing the need for direct motion imitation.
GUPO reveals that accounting for gradient uncertainty can dramatically improve policy optimization in post-training LLMs, leading to more effective reasoning capabilities.