title: ReasonGen-R1: CoT for Autoregressive Image generation models through SFT
    and RL
summary: 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.
paper_authors._id: 683dc7e8dd3bccdc9a2ec6bc
paper_authors.name: Yu Zhang
paper_authors._id: 683dc7e8dd3bccdc9a2ec6bd
paper_authors.name: Yunqi Li
paper_authors._id: 683dc7e8dd3bccdc9a2ec6be
paper_authors.name: Yifan Yang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-02T15:48:59.450Z
paper_authors._id: 62d18eb81e36881a57f29bf4
paper_authors.avatarUrl: /avatars/104851421b4ee9641daaf15942fa7ea1.svg
paper_authors.fullname: Yif Yang
paper_authors.type: user
paper_authors.user: Yif29
paper_authors._id: 683dc7e8dd3bccdc9a2ec6bf
paper_authors.name: Rui Wang
paper_authors._id: 683dc7e8dd3bccdc9a2ec6c0
paper_authors.name: Yuqing Yang
paper_authors._id: 683dc7e8dd3bccdc9a2ec6c1
paper_authors.name: Dai Qi
paper_authors._id: 683dc7e8dd3bccdc9a2ec6c2
paper_authors.name: Jianmin Bao
paper_authors._id: 683dc7e8dd3bccdc9a2ec6c3
paper_authors.name: Dongdong Chen
paper_authors._id: 683dc7e8dd3bccdc9a2ec6c4
paper_authors.name: Chong Luo
paper_authors._id: 683dc7e8dd3bccdc9a2ec6c5
paper_authors.name: Lili Qiu
paper_title: ReasonGen-R1: CoT for Autoregressive Image generation models through SFT
    and RL
paper_summary: 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.
paper_ai_summary: A two-stage framework ReasonGen-R1 integrates chain-of-thought reasoning and reinforcement learning to enhance image generation by imbuing models with text-based thinking skills and refining outputs through reward optimization.
paper_ai_keywords: autoregressive image generator supervised fine-tuning reasoning dataset Group Relative Policy Optimization model crafted rationales pretrained vision language model reward signals GenEval DPG T2I benchmark