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MultiMDM achieves superior few-step generation by cleverly preserving the masking structure, enabling a drafting capability that enhances token refinement.
By compressing future observations into a learned "condition space," WoG enables VLA models to generate more precise actions and generalize better than models relying on direct future prediction.
