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arXiv · 2509.25681

dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought

Junjie Wen·Minjie Zhu·Jiaming Liu·Zhiyuan Liu·Yicun Yang·Linfeng Zhang·Shanghang Zhang·Yichen Zhu·Yi Xu
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Abstract

Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics. We introduce dVLA, a diffusion-based VLA that leverages a multimodal chain-of-thought to unify visual perception, language reasoning, and robotic control in a single system. dVLA jointly optimizes perception, language understanding, and action under a single diffusion objective, enabling stronger cross-modal reasoning and better generalization to novel instructions and objects. For practical deployment, we mitigate inference latency by incorporating two acceleration strategies, a prefix attention mask and KV caching, yielding up to around times speedup at test-time inference. We evaluate dVLA in both simulation and the real world: on the LIBERO benchmark, it achieves state-of-the-art performance with a 96.4% average success rate, consistently surpassing both discrete and continuous action policies; on a real Franka robot, it succeeds across a diverse task suite, including a challenging bin-picking task that requires multi-step planning, demonstrating robust real-world performance. Together, these results underscore the promise of unified diffusion frameworks for practical, high-performance VLA robotics.

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Junjie Wen, Minjie Zhu, Jiaming Liu, Zhiyuan Liu, Yicun Yang, Linfeng Zhang, Shanghang Zhang, Yichen Zhu, Yi Xu. 2025-09-30. dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought. https://arxiv.org/abs/2509.25681

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