title: T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level
    and Token-level CoT
summary: Recent advancements in large language models have demonstrated how
  chain-of-thought (CoT) and reinforcement learning (RL) can improve performance.
  However, applying such reasoning strategies to the visual generation domain
  remains largely unexplored. In this paper, we present T2I-R1, a novel
  reasoning-enhanced text-to-image generation model, powered by RL with a
  bi-level CoT reasoning process. Specifically, we identify two levels of CoT
  that can be utilized to enhance different stages of generation: (1) the
  semantic-level CoT for high-level planning of the prompt and (2) the
  token-level CoT for low-level pixel processing during patch-by-patch
  generation. To better coordinate these two levels of CoT, we introduce
  BiCoT-GRPO with an ensemble of generation rewards, which seamlessly optimizes
  both generation CoTs within the same training step. By applying our reasoning
  strategies to the baseline model, Janus-Pro, we achieve superior performance
  with 13% improvement on T2I-CompBench and 19% improvement on the WISE
  benchmark, even surpassing the state-of-the-art model FLUX.1. Code is available
  at: https://github.com/CaraJ7/T2I-R1
paper_authors._id: 681428debcdf962d03da2797
paper_authors.name: Dongzhi Jiang
paper_authors.status: claimed_verified
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paper_authors.fullname: Dongzhi Jiang
paper_authors.type: user
paper_authors.user: CaraJ
paper_authors._id: 681428debcdf962d03da2798
paper_authors.name: Ziyu Guo
paper_authors.status: admin_assigned
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paper_authors.fullname: Ziyu Guo
paper_authors.type: user
paper_authors.user: ZiyuG
paper_authors._id: 681428debcdf962d03da2799
paper_authors.name: Renrui Zhang
paper_authors._id: 681428debcdf962d03da279a
paper_authors.name: Zhuofan Zong
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paper_authors.fullname: Zhuofan Zong
paper_authors.type: user
paper_authors.user: zongzhuofan
paper_authors._id: 681428debcdf962d03da279b
paper_authors.name: Hao Li
paper_authors._id: 681428debcdf962d03da279c
paper_authors.name: Le Zhuo
paper_authors.status: admin_assigned
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paper_authors._id: 6358a167f56b03ec9147074d
paper_authors.avatarUrl: /avatars/e54ea7bf0c240cf76d538296efb3976c.svg
paper_authors.fullname: Le Zhuo
paper_authors.type: user
paper_authors.user: JackyZhuo
paper_authors._id: 681428debcdf962d03da279d
paper_authors.name: Shilin Yan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:45:11.106Z
paper_authors._id: 65273fea0ef49cfb783fa5c1
paper_authors.avatarUrl: /avatars/0c9e204bc2151c8cc533311900d05a36.svg
paper_authors.fullname: shilinyan
paper_authors.type: user
paper_authors.user: shilinyan
paper_authors._id: 681428debcdf962d03da279e
paper_authors.name: Pheng-Ann Heng
paper_authors._id: 681428debcdf962d03da279f
paper_authors.name: Hongsheng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:45:27.964Z
paper_authors._id: 65c04e9c27a5fdca81abcbd9
paper_authors.avatarUrl: /avatars/12a155683c824fa23da4a9e2bed4f64e.svg
paper_authors.fullname: Hongsheng LI
paper_authors.type: user
paper_authors.user: hsli-cuhk
paper_title: T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level
    and Token-level CoT
paper_summary: Recent advancements in large language models have demonstrated how
  chain-of-thought (CoT) and reinforcement learning (RL) can improve performance.
  However, applying such reasoning strategies to the visual generation domain
  remains largely unexplored. In this paper, we present T2I-R1, a novel
  reasoning-enhanced text-to-image generation model, powered by RL with a
  bi-level CoT reasoning process. Specifically, we identify two levels of CoT
  that can be utilized to enhance different stages of generation: (1) the
  semantic-level CoT for high-level planning of the prompt and (2) the
  token-level CoT for low-level pixel processing during patch-by-patch
  generation. To better coordinate these two levels of CoT, we introduce
  BiCoT-GRPO with an ensemble of generation rewards, which seamlessly optimizes
  both generation CoTs within the same training step. By applying our reasoning
  strategies to the baseline model, Janus-Pro, we achieve superior performance
  with 13% improvement on T2I-CompBench and 19% improvement on the WISE
  benchmark, even surpassing the state-of-the-art model FLUX.1. Code is available
  at: https://github.com/CaraJ7/T2I-R1
paper_ai_summary: T2I-R1, a reasoning-enhanced text-to-image generator using RL and bi-level chain-of-thought reasoning, improves performance by 13% on T2I-CompBench and 19% on WISE compared to Janus-Pro.
paper_ai_keywords: chain-of-thought reinforcement learning text-to-image generation semantic-level CoT token-level CoT BiCoT-GRPO generation rewards T2I-CompBench WISE FLUX