Continuous Reasoning for Vision-Language-Action
Natural language is a powerful reasoning medium for language and vision-language models, but it is mismatched to the granularity of continuous control. Text and explicit subgoals operate at task-level granularity, whereas vision-language-action (VLA) policies must choose actions at a much finer temporal scale; a single reasoning step can therefore span many action chunks while remaining only weakly coupled to the action needed now. This suggests a different question for VLA: what should play the role of language? We argue that a useful VLA reasoning medium must be shareable across model instances, verifiable through downstream action improvement, and aligned with temporally extended contr...
| Authors | Yueh-Hua Wu, Tatsuya Matsushima, Kei Ota |
|---|---|
| Primary category | cs.RO |
| Published | 29 May 2026 |
| Last updated at source | 8 June 2026 |
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