[2511.21542] E0: Enhancing Generalization and Fine-Grained Control in VLA Models via Tweedie Discrete Diffusion

Computer Science > Robotics arXiv:2511.21542 (cs) [Submitted on 26 Nov 2025 (v1), last revised 25 Mar 2026 (this version, v2)] Title:E0: Enhancing Generalization and Fine-Grained Control in VLA Models via Tweedie Discrete Diffusion Authors:Zhihao Zhan, Jiaying Zhou, Likui Zhang, Qinhan Lv, Hao Liu, Jusheng Zhang, Weizheng Li, Ziliang Chen, Tianshui Chen, Ruifeng Zhai, Keze Wang, Liang Lin, Guangrun Wang View a PDF of the paper titled E0: Enhancing Generalization and Fine-Grained Control in VLA Models via Tweedie Discrete Diffusion, by Zhihao Zhan and 12 other authors View PDF HTML (experimental) Abstract:Vision-Language-Action (VLA) models offer a unified framework for robotic manipulation by integrating visual perception, language understanding, and control generation. However, existing VLA systems still struggle to generalize across diverse tasks, scenes, and camera viewpoints, and often produce coarse or unstable actions. We argue that these limitations are closely tied to the structural properties of actions in VLA settings, including the inherent multi-peaked nature of action distributions, the token-based symbolic reasoning of pretrained VLM/VLA backbones, and the effective finite resolution imposed by real-world robotic control. Motivated by these properties, we introduce E0, a tweedie discrete diffusion framework that formulates action generation as iterative denoising over quantized action tokens. By operating in a discrete action space with a principled diffusion...