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Rethinking few-step distillation reveals that the training pipeline's organization is as crucial as the distillation objectives themselves.
Robot RL training can be dramatically sped up (3-10x) by decoupling CPU-based simulation from GPU-based learning, challenging the assumption that GPU-resident physics is essential for efficiency.
