DiMaS: Distribution Matching for Steering Vision-Language-Action Models
DiMaS is a distribution-matching steering strategy for flow-matching vision-language-action (VLA) models, enabling fine-grained behavioral control in robotic manipulation. It transports between representation distributions rather than shifting along a fixed direction, proving effective on two state-of-the-art VLAs. The study also examines transferability and explains why linear steering fails in visuomotor settings: behavioral features are linearly decodable but not linearly steerable.
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[Submitted on 15 Jul 2026]
Title:DiMaS: Distribution Matching for Steering Vision-Language-Action Models
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Abstract:Flow-matching-based vision-language-action (VLA) models have emerged as powerful policies for robotic manipulation, yet a critical capability remains underexplored: fine-grained behavioral control, the ability to govern how a robot performs a task by intervening on its internal representations. Representation steering is a well-established interpretability tool for language and vision-language models, where behavioral features are typically encoded as linear directions, but we show that these classic methods fall short in VLAs. We propose DiMaS, a Distribution-Matching Steering strategy tailored to flow-matching VLAs, which transports between representation distributions rather than shifting along a fixed direction, and show that it effectively controls behavior across two state-of-the-art VLAs. We further examine the generalizability of this strategy as the tasks it is learned from and evaluated on grow increasingly dissimilar, characterizing where behavioral control transfers and where it weakens. Finally, through an analysis of the representation structure of the action expert, we explain why classical linear steering falls short in the visuomotor setting: behavioral features are linearly decodable but not linearly steerable, which motivates the distribution-matching design of DiMaS. Our code is publicly available at this https URL, with additional results and videos at this https URL
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2607.14280 [cs.RO]
(or arXiv:2607.14280v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.14280
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Pegah Khayatan [view email] [v1] Wed, 15 Jul 2026 18:39:13 UTC (1,268 KB)
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