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KineBench reveals that embodied world models exhibit task-complexity-bounded nonlinear scaling, challenging existing assumptions about model performance and data requirements.
By pretraining a VLA model with goal-conditioned RL, PRTS learns to reason about goal reachability, leading to substantial gains in long-horizon robotic tasks and zero-shot generalization.
