X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image
  Generative Models Great Again
Authors: name: Zigang Geng, name: Yibing Wang, name: Yeyao Ma, name: Chen Li, name: Yongming Rao, name: Shuyang Gu, name: Zhao Zhong, name: Qinglin Lu, name: Han Hu, name: Xiaosong Zhang, name: Linus, name: Di Wang, name: Jie Jiang

Numerous efforts have been made to extend the ``next token prediction''
paradigm to visual contents, aiming to create a unified approach for both image
generation and understanding. Nevertheless, attempts to generate images through
autoregressive modeling with discrete tokens have been plagued by issues such
as low visual fidelity, distorted outputs, and failure to adhere to complex
instructions when rendering intricate details. These shortcomings are likely
attributed to cumulative errors during autoregressive inference or information
loss incurred during the discretization process. Probably due to this
challenge, recent research has increasingly shifted toward jointly training
image generation with diffusion objectives and language generation with
autoregressive objectives, moving away from unified modeling approaches. In
this work, we demonstrate that reinforcement learning can effectively mitigate
artifacts and largely enhance the generation quality of a discrete
autoregressive modeling method, thereby enabling seamless integration of image
and language generation. Our framework comprises a semantic image tokenizer, a
unified autoregressive model for both language and images, and an offline
diffusion decoder for image generation, termed X-Omni. X-Omni achieves
state-of-the-art performance in image generation tasks using a 7B language
model, producing images with high aesthetic quality while exhibiting strong
capabilities in following instructions and rendering long texts.