Unified Diffusion VLA: Vision-Language-Action Model via Joint Discrete
  Denoising Diffusion Process
Authors: name: Jiayi Chen, name: Wenxuan Song, name: Pengxiang Ding, name: Ziyang Zhou, name: Han Zhao, name: Feilong Tang, name: Donglin Wang, name: Haoang Li
Organization: HKUSTGZ

Vision-language-action (VLA) models aim to understand natural language
instructions and visual observations and to execute corresponding actions as an
embodied agent. Recent work integrates future images into the
understanding-acting loop, yielding unified VLAs that jointly understand,
generate, and act -- reading text and images and producing future images and
actions. However, these models either rely on external experts for modality
unification or treat image generation and action prediction as separate
processes, limiting the benefits of direct synergy between these tasks. Our
core philosophy is to optimize generation and action jointly through a
synchronous denoising process, where the iterative refinement enables actions
to evolve from initialization, under constant and sufficient visual guidance.
We ground this philosophy in our proposed Unified Diffusion VLA and Joint
Discrete Denoising Diffusion Process (JD3P), which is a joint diffusion process
that integrates multiple modalities into a single denoising trajectory to serve
as the key mechanism enabling understanding, generation, and acting to be
intrinsically synergistic. Our model and theory are built on a unified
tokenized space of all modalities and a hybrid attention mechanism. We further
propose a two-stage training pipeline and several inference-time techniques
that optimize performance and efficiency. Our approach achieves
state-of-the-art performance on benchmarks such as CALVIN, LIBERO, and
SimplerEnv with 4times faster inference than autoregressive methods, and we
demonstrate its effectiveness through in-depth analysis and real-world
evaluations. Our project page is available at
https://irpn-eai.github.io/UD-VLA.github.io/.