[Submitted on 3 Jul 2026]
Title:DREAMSTEER: Latent World Models Can Steer VLA Policies During Deployment Without Any Finetuning
View a PDF of the paper titled DREAMSTEER: Latent World Models Can Steer VLA Policies During Deployment Without Any Finetuning, by Hanchen Cui and 7 other authors
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Abstract:Pretrained vision-language-action (VLA) policies show promising zero-shot generalization, but often fail under deployment-time distribution shift, leading to decreased robustness and inconsistent instruction following. While prior work commonly tackles this by finetuning on in-distribution data, it assumes demonstrations collected on tasks in the target environment. In this work, we propose DREAMSTEER, a deployment-time steering framework for pretrained VLAs without any finetuning or parameter modifications. The key insight in DREAMSTEER is to leverage a latent world model and a value model to steer pretrained VLA policies. During deployment, DREAMSTEER samples candidate action chunks from a VLA policy and predefined motion primitives, imagines their outcomes using an action-conditioned latent world model, and ranks the imagined trajectories with a language-conditioned value model. Across four real-world manipulation benchmarks with unseen objects, DREAMSTEER improves task success rate from 23.75% to 66.25% and instruction-following accuracy from 38.75% to 56.25% over the base VLA policy.
Comments: 19 pages, 12 figures. Project page: this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.02865 [cs.RO]
(or arXiv:2607.02865v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.02865
arXiv-issued DOI via DataCite
Submission history
From: Hanchen Cui [view email] [v1] Fri, 3 Jul 2026 02:08:50 UTC (32,117 KB)
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