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 can be separated. What a world model knows about physical scenes lives in its internal features; generating the future is merely the objective that produced them, so the grounding can be inherited while the generative machinery is left behind. We add one feature-alignment term to ordinary VLA training: a frozen wor...
| Authors | Trung Dao, Sankalp Yamsani, Jaden Park et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 September 2026 |
| Last updated at source | 9 October 2026 |
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