Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies
Vision-Language-Action (VLA) models map observations to actions with no objective that accounts for how the world responds, so their robustness is bounded primarily by data coverage. World models carry precisely that missing objective and are better grounded for it, yet rolling the future forward costs seconds per decision and rules them out of the control loop. We show the two
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Vision-Language-Action (VLA) models map observations to actions with no objective that accounts for how the world responds, so their robustness is bounded primarily by data coverage. World models carry precisely that missing objective and are better grounded for it, yet rolling the future forward costs seconds per decision and rules them out of the control loop. We show the two
This research addresses the critical challenge of making sophisticated world models practical for real-time robotic control by distilling their knowledge into more efficient Vision-Language-Action (VLA) policies. For ML engineers, this means developing more robust and deployable robot agents without the computational overhead of full world models.