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.

翻译：视觉-语言-动作（VLA）模型旨在通过视觉观察和自然语言指令控制机器人进行操控。然而，现有的分层式与自回归范式常引入架构开销，存在时序不一致性及长程误差累积问题，且缺乏无需额外模块即可捕捉环境动态的机制。为此，我们提出MMaDA-VLA，一个完全原生预训练的大规模扩散VLA模型，将多模态理解与生成统一于单一框架中。其核心思想是采用原生离散扩散公式，将语言、图像和连续机器人控制信号嵌入统一离散词元空间，并通过带掩码词元去噪训练单一骨干网络，并行生成未来目标观测与动作片段。迭代去噪过程实现全局无序优化，提升长程一致性，同时无需辅助世界模型即可基于预测的未来视觉结果驱动动作。在仿真基准与真实任务上的实验表明，该方法达到最优性能：LIBERO任务平均成功率98.0%，CALVIN任务平均路径长度4.78。