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MVUCF achieves a remarkable 98.9% on LIBERO, showcasing a 22.4-point improvement on LIBERO-Plus and a 23.3-point increase in task success rates, all without additional inference costs.
By jointly modeling video dynamics and actions, DiT4DiT achieves 10x sample efficiency and 7x faster convergence in robot policy learning, showing that video generation can be a powerful scaling proxy.
