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.
@article{arxiv.2509.25681,
title = {dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought},
author = {Junjie Wen and Minjie Zhu and Jiaming Liu and Zhiyuan Liu and Yicun Yang and Linfeng Zhang and Shanghang Zhang and Yichen Zhu and Yi Xu},
journal= {arXiv preprint arXiv:2509.25681},
year = {2025}
}