ReasonGen-R1: CoT for Autoregressive Image generation models through SFT
  and RL
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.
683dc7e8dd3bccdc9a2ec6bc
Yu Zhang
683dc7e8dd3bccdc9a2ec6bd
Yunqi Li
683dc7e8dd3bccdc9a2ec6be
Yifan Yang
extracted_confirmed
2025-08-01T06:10:05.212Z
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/avatars/104851421b4ee9641daaf15942fa7ea1.svg
Yif Yang
user
Yif29
683dc7e8dd3bccdc9a2ec6bf
Rui Wang
683dc7e8dd3bccdc9a2ec6c0
Yuqing Yang
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Dai Qi
683dc7e8dd3bccdc9a2ec6c2
Jianmin Bao
683dc7e8dd3bccdc9a2ec6c3
Dongdong Chen
683dc7e8dd3bccdc9a2ec6c4
Chong Luo
683dc7e8dd3bccdc9a2ec6c5
Lili Qiu
ReasonGen-R1: CoT for Autoregressive Image generation models through SFT
  and RL
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.
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.
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