MMaDA-VLA: Large Diffusion Vision-Language-Action Model with Unified Multi-Modal Instruction and Generation
Vision-Language-Action (VLA) models aim to control robots for manipulation from visual observations and natural-language instructions. However, existing hierarchical and autoregressive paradigms often introduce architectural overhead, suffer from temporal inconsistency and long-horizon error accumulation, and lack a mechanism to capture environment dynamics without extra modules. To this end, we present MMaDA-VLA, a fully native pre-trained large diffusion VLA model that unifies multi-modal understanding and generation in a single framework. Our key idea is a native discrete diffusion formulation that embeds language, images, and continuous robot controls into one discrete token space and trains a single backbone with masked token denoising to jointly generate a future goal observation and an action chunk in parallel. Iterative denoising enables global, order-free refinement, improving long-horizon consistency while grounding actions in predicted future visual outcomes without auxiliary world models. Experiments across simulation benchmarks and real-world tasks show state-of-the-art performance, achieving 98.0% average success on LIBERO and 4.78 average length on CALVIN.
69cdd313acf91530b21df922
Yang Liu
claimed_verified
2026-04-02T08:13:20.346Z
6586817e509bcae23f3dfc60
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pwEStoey1XJYDi3F0Z930.png
Yang Liu
yliu-cs
user
yliu-cs
69cdd313acf91530b21df923
Pengxiang Ding
69cdd313acf91530b21df924
Tengyue Jiang
69cdd313acf91530b21df925
Xudong Wang
69cdd313acf91530b21df926
Wenxuan Song
69cdd313acf91530b21df927
Minghui Lin
69cdd313acf91530b21df928
Han Zhao
69cdd313acf91530b21df929
Hongyin Zhang
69cdd313acf91530b21df92a
Zifeng Zhuang
69cdd313acf91530b21df92b
Wei Zhao
69cdd313acf91530b21df92c
Siteng Huang
69cdd313acf91530b21df92d
Jinkui Shi
69cdd313acf91530b21df92e
Donglin Wang
MMaDA-VLA: Large Diffusion Vision-Language-Action Model with Unified Multi-Modal Instruction and Generation
Vision-Language-Action (VLA) models aim to control robots for manipulation from visual observations and natural-language instructions. However, existing hierarchical and autoregressive paradigms often introduce architectural overhead, suffer from temporal inconsistency and long-horizon error accumulation, and lack a mechanism to capture environment dynamics without extra modules. To this end, we present MMaDA-VLA, a fully native pre-trained large diffusion VLA model that unifies multi-modal understanding and generation in a single framework. Our key idea is a native discrete diffusion formulation that embeds language, images, and continuous robot controls into one discrete token space and trains a single backbone with masked token denoising to jointly generate a future goal observation and an action chunk in parallel. Iterative denoising enables global, order-free refinement, improving long-horizon consistency while grounding actions in predicted future visual outcomes without auxiliary world models. Experiments across simulation benchmarks and real-world tasks show state-of-the-art performance, achieving 98.0% average success on LIBERO and 4.78 average length on CALVIN.
A native discrete diffusion framework unifies multi-modal understanding and generation for robotic manipulation, enabling parallel action and visual outcome prediction with improved long-horizon consistency.
vision-language-action models large diffusion models discrete diffusion formulation masked token denoising iterative denoising multi-modal understanding robot control visual outcome prediction long-horizon consistency