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arXiv AI By Parsa Mastouri Kashani, Jan-Gerrit Habekost, Stefan Wermter

Towards VLA-Dreamer: Refining VLA Behavior Using World Models

Read the original on arXiv AI →

The paper proposes a new architecture for Vision‑Language‑Action (VLA) models that improves sample efficiency by training a predictive world model on the vision encoder’s embedding space. It argues that these embeddings are action‑relevant and can be used to predict future states, addressing the lack of an explicit world model in current VLAs. The trained model can also support short‑term planning by sampling actions that lead to desired goal images.

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 AI.

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