title: GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation
    with Reinforcement Learning
summary: Visual generation models have made remarkable progress in creating realistic
  images from text prompts, yet struggle with complex prompts that specify
  multiple objects with precise spatial relationships and attributes. Effective
  handling of such prompts requires explicit reasoning about the semantic content
  and spatial layout. We present GoT-R1, a framework that applies reinforcement
  learning to enhance semantic-spatial reasoning in visual generation. Building
  upon the Generation Chain-of-Thought approach, GoT-R1 enables models to
  autonomously discover effective reasoning strategies beyond predefined
  templates through carefully designed reinforcement learning. To achieve this,
  we propose a dual-stage multi-dimensional reward framework that leverages MLLMs
  to evaluate both the reasoning process and final output, enabling effective
  supervision across the entire generation pipeline. The reward system assesses
  semantic alignment, spatial accuracy, and visual quality in a unified approach.
  Experimental results demonstrate significant improvements on T2I-CompBench
  benchmark, particularly in compositional tasks involving precise spatial
  relationships and attribute binding. GoT-R1 advances the state-of-the-art in
  image generation by successfully transferring sophisticated reasoning
  capabilities to the visual generation domain. To facilitate future research, we
  make our code and pretrained models publicly available at
  https://github.com/gogoduan/GoT-R1.
paper_authors._id: 682ffa9a6e906040a3bb7160
paper_authors.name: Chengqi Duan
paper_authors._id: 682ffa9a6e906040a3bb7161
paper_authors.name: Rongyao Fang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:15:13.374Z
paper_authors._id: 65b8724123d948d884b379b1
paper_authors.avatarUrl: /avatars/ce189d1d8d688c17912f9b869035b2d0.svg
paper_authors.fullname: Rongyao Fang
paper_authors.type: user
paper_authors.user: LucasFang
paper_authors._id: 682ffa9a6e906040a3bb7162
paper_authors.name: Yuqing Wang
paper_authors._id: 682ffa9a6e906040a3bb7163
paper_authors.name: Kun Wang
paper_authors._id: 682ffa9a6e906040a3bb7164
paper_authors.name: Linjiang Huang
paper_authors._id: 682ffa9a6e906040a3bb7165
paper_authors.name: Xingyu Zeng
paper_authors._id: 682ffa9a6e906040a3bb7166
paper_authors.name: Hongsheng Li
paper_authors._id: 682ffa9a6e906040a3bb7167
paper_authors.name: Xihui Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:15:11.367Z
paper_authors._id: 65d5ec74cd05bc1eaa125040
paper_authors.avatarUrl: /avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_title: GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation
    with Reinforcement Learning
paper_summary: Visual generation models have made remarkable progress in creating realistic
  images from text prompts, yet struggle with complex prompts that specify
  multiple objects with precise spatial relationships and attributes. Effective
  handling of such prompts requires explicit reasoning about the semantic content
  and spatial layout. We present GoT-R1, a framework that applies reinforcement
  learning to enhance semantic-spatial reasoning in visual generation. Building
  upon the Generation Chain-of-Thought approach, GoT-R1 enables models to
  autonomously discover effective reasoning strategies beyond predefined
  templates through carefully designed reinforcement learning. To achieve this,
  we propose a dual-stage multi-dimensional reward framework that leverages MLLMs
  to evaluate both the reasoning process and final output, enabling effective
  supervision across the entire generation pipeline. The reward system assesses
  semantic alignment, spatial accuracy, and visual quality in a unified approach.
  Experimental results demonstrate significant improvements on T2I-CompBench
  benchmark, particularly in compositional tasks involving precise spatial
  relationships and attribute binding. GoT-R1 advances the state-of-the-art in
  image generation by successfully transferring sophisticated reasoning
  capabilities to the visual generation domain. To facilitate future research, we
  make our code and pretrained models publicly available at
  https://github.com/gogoduan/GoT-R1.
paper_ai_summary: GoT-R1 enhances visual generation by using reinforcement learning to improve semantic-spatial reasoning, outperforming existing models on complex compositional tasks.
paper_ai_keywords: reinforcement learning Generation Chain-of-Thought MLLMs dual-stage multi-dimensional reward framework semantic alignment spatial accuracy visual quality T2I-CompBench