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Generative world action models can run up to 3× faster in closed-loop control without fine-tuning or task degradation by dynamically repairing and reusing intermediate computational states instead of recomputing them from scratch.
Pruning data with MCL not only boosts efficiency but also reveals hidden semantic structures that traditional methods overlook.
