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RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning

Mingrui Wu, Lu Wang, Pu Zhao, Fangkai Yang, Jianjin Zhang, Jianfeng Liu, Yuefeng Zhan, Weihao Han, Hao Sun, Jiayi Ji, Xiaoshuai Sun, Qingwei Lin, Weiwei Deng, Dongmei Zhang, Feng Sun, Qi Zhang, Rongrong Ji

2025-05-26

RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation
  via Reinforcement Learning

Summary

This paper talks about RePrompt, a new technique that helps AI models create better images from text descriptions by teaching them to reason and adjust their prompts for improved results.

What's the problem?

The problem is that when you ask an AI to generate an image from a text description, it often struggles to get the layout and details right, especially when the description is complicated or involves several objects and relationships.

What's the solution?

The researchers designed RePrompt, a system that uses reinforcement learning to let the AI try different prompts and learn which ones lead to better images. This approach helps the model understand how to arrange things in the picture and handle more complex descriptions.

Why it matters?

This is important because it means AI can create images that better match what people ask for, making text-to-image tools much more useful for art, design, education, and creative projects.

Abstract

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.