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arXiv Computer Vision By Trung Dao, Sankalp Yamsani, Jaden Park, Joohyung Kim, Yong Jae Lee

Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies

Read the original on arXiv Computer Vision →

The paper proposes a method to distill world‑model representations into compact Vision‑Language‑Action (VLA) policies. By adding a single feature‑alignment term during VLA training, a frozen world model’s internal features are cached and the student policy learns to match them, eliminating the need for a generative future‑rolling component. The resulting lightweight policy runs in 32 ms on an RTX 5090, achieving high performance on LIBERO and RoboCasa‑GR1, and transfers effectively to real robotic hardware.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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