ReasonGen-R1: CoT for Autoregressive Image generation models through SFT
  and RL
Authors: name: Yu Zhang, name: Yunqi Li, name: Yifan Yang, name: Rui Wang, name: Yuqing Yang, name: Dai Qi, name: Jianmin Bao, name: Dongdong Chen, name: Chong Luo, name: Lili Qiu

Although chain-of-thought reasoning and reinforcement learning (RL) have
driven breakthroughs in NLP, their integration into generative vision models
remains underexplored. We introduce ReasonGen-R1, a two-stage framework that
first imbues an autoregressive image generator with explicit text-based
"thinking" skills via supervised fine-tuning on a newly generated reasoning
dataset of written rationales, and then refines its outputs using Group
Relative Policy Optimization. To enable the model to reason through text before
generating images, We automatically generate and release a corpus of model
crafted rationales paired with visual prompts, enabling controlled planning of
object layouts, styles, and scene compositions. Our GRPO algorithm uses reward
signals from a pretrained vision language model to assess overall visual
quality, optimizing the policy in each update. Evaluations on GenEval, DPG, and
the T2I benchmark demonstrate that ReasonGen-R1 consistently outperforms strong
baselines and prior state-of-the-art models. More: aka.ms/reasongen.