AI画图里的字终于能看清了：用被淘汰的废片反哺模型
AI生成带文字的图片一直是个老大难——要么字糊成一团，要么拼写错误、位置乱飘。现有做法是：先海量收集图片，筛掉不合格的，留下好的训练。但那些被筛掉的“废片”其实藏着宝贵信息：哪里OCR识别错了、哪里语义对不上。这篇论文搞了个多智能体系统，让一个智能体专门收集样本，一个负责打分并记录失败原因，一个总结本轮失败规律，最后一个根据规律去补生成那些模型最弱的场景。下一轮训练时，这些反馈会指导数据采集，避免重复踩坑。在PixArt-alpha模型上，只用75万张图，就把文字识别准确率（OCR-F1）在TextScenesHQ上提升了85.3%，在LongTextBench上提升了35.3%。效果还迁移到了另一个模型Show-o2上。这不是你明天就能用的工具，但它揭示了一个趋势：AI训练数据的价值不在“多”，而在“怎么从错误中学习”。
📄 原文摘要(英文)
Text-rich image generation is one of the most challenging settings in image generation, since models must simultaneously produce visually realistic images and render legible, semantically aligned, and layout-consistent text. Existing data pipelines usually follow a static crawl-filter-freeze paradigm. They collect candidate samples, filter them once, and freeze the accepted data for training. However, rejected samples are usually discarded, although they often contain useful failure signals such as OCR errors and semantic mismatches. As a result, later construction rounds may repeat the same failure modes. To address these limitations, we propose DataEvolver, a self-evolving multi-agent framework for text-rich image data construction. DataEvolver treats data construction as feedback-driven construction policy evolution. A Retriever collects candidate samples, a Verifier assigns quality scores and rejection causes, a Critic summarizes round-level feedback into semantic feedback, and a Generator completes under-covered regions through targeted synthesis. The updated feedback memory then guides the next construction round. Experiments on text-rich image generation benchmarks show that DataEvolver produces more useful training data than fixed-dataset baselines under matched data budgets. At the 0.75M scale on PixArt-alpha, DataEvolver improves OCR-F1 over the strongest baseline by 85.3 percent on TextScenesHQ and 35.3 percent on LongTextBench. The improvements are consistent across both evaluated benchmarks and also transfer to Show-o2, indicating that the benefit of DataEvolver is not tied to a single downstream generator. These results suggest that rejected samples can provide actionable feedback for improving text-rich image data construction.