title: RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation
    via Reinforcement Learning
summary: Despite recent progress in text-to-image (T2I) generation, existing models
  often struggle to faithfully capture user intentions from short and
  under-specified prompts. While prior work has attempted to enhance prompts
  using large language models (LLMs), these methods frequently generate stylistic
  or unrealistic content due to insufficient grounding in visual semantics and
  real-world composition. Inspired by recent advances in reasoning for language
  model, we propose RePrompt, a novel reprompting framework that introduces
  explicit reasoning into the prompt enhancement process via reinforcement
  learning. Instead of relying on handcrafted rules or stylistic rewrites, our
  method trains a language model to generate structured, self-reflective prompts
  by optimizing for image-level outcomes. The tailored reward models assesse the
  generated images in terms of human preference, semantic alignment, and visual
  composition, providing indirect supervision to refine prompt generation. Our
  approach enables end-to-end training without human-annotated data. Experiments
  on GenEval and T2I-Compbench show that RePrompt significantly boosts spatial
  layout fidelity and compositional generalization across diverse T2I backbones,
  establishing new state-of-the-art results.
paper_authors._id: 683409de1869c47bd0c423a4
paper_authors.name: Mingrui Wu
paper_authors._id: 683409de1869c47bd0c423a5
paper_authors.name: Lu Wang
paper_authors._id: 683409de1869c47bd0c423a6
paper_authors.name: Pu Zhao
paper_authors._id: 683409de1869c47bd0c423a7
paper_authors.name: Fangkai Yang
paper_authors._id: 683409de1869c47bd0c423a8
paper_authors.name: Jianjin Zhang
paper_authors._id: 683409de1869c47bd0c423a9
paper_authors.name: Jianfeng Liu
paper_authors._id: 683409de1869c47bd0c423aa
paper_authors.name: Yuefeng Zhan
paper_authors._id: 683409de1869c47bd0c423ab
paper_authors.name: Weihao Han
paper_authors._id: 683409de1869c47bd0c423ac
paper_authors.name: Hao Sun
paper_authors._id: 683409de1869c47bd0c423ad
paper_authors.name: Jiayi Ji
paper_authors._id: 683409de1869c47bd0c423ae
paper_authors.name: Xiaoshuai Sun
paper_authors._id: 683409de1869c47bd0c423af
paper_authors.name: Qingwei Lin
paper_authors._id: 683409de1869c47bd0c423b0
paper_authors.name: Weiwei Deng
paper_authors._id: 683409de1869c47bd0c423b1
paper_authors.name: Dongmei Zhang
paper_authors._id: 683409de1869c47bd0c423b2
paper_authors.name: Feng Sun
paper_authors._id: 683409de1869c47bd0c423b3
paper_authors.name: Qi Zhang
paper_authors._id: 683409de1869c47bd0c423b4
paper_authors.name: Rongrong Ji
paper_title: RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation
    via Reinforcement Learning
paper_summary: Despite recent progress in text-to-image (T2I) generation, existing models
  often struggle to faithfully capture user intentions from short and
  under-specified prompts. While prior work has attempted to enhance prompts
  using large language models (LLMs), these methods frequently generate stylistic
  or unrealistic content due to insufficient grounding in visual semantics and
  real-world composition. Inspired by recent advances in reasoning for language
  model, we propose RePrompt, a novel reprompting framework that introduces
  explicit reasoning into the prompt enhancement process via reinforcement
  learning. Instead of relying on handcrafted rules or stylistic rewrites, our
  method trains a language model to generate structured, self-reflective prompts
  by optimizing for image-level outcomes. The tailored reward models assesse the
  generated images in terms of human preference, semantic alignment, and visual
  composition, providing indirect supervision to refine prompt generation. Our
  approach enables end-to-end training without human-annotated data. Experiments
  on GenEval and T2I-Compbench show that RePrompt significantly boosts spatial
  layout fidelity and compositional generalization across diverse T2I backbones,
  establishing new state-of-the-art results.
paper_ai_summary: RePrompt, a reprompting framework using reinforcement learning, enhances text-to-image generation by optimizing for image-level outcomes, significantly improving spatial layout and compositional generalization.
paper_ai_keywords: text-to-image T2I large language models LLMs reinforcement learning structured prompts self-reflective prompts reward models human preference semantic alignment visual composition GenEval T2I-Compbench spatial layout fidelity compositional generalization