用强化学习让文档解析模型更懂版式，提升结构化准确率。
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing
- 基于视觉-语言模型的端到端强化学习框架，显式优化版式感知能力。
- 在55K合成+真实文档数据上训练，多项任务达到新SOTA，读序保持更准。
- 适合需要高精度版式理解的文档自动化场景，如合同、报表处理。
扫描文档的自动化解析仍是文档人工智能的关键瓶颈，传统多阶段流水线易产生误差传播且难以适应多样版式。本文提出layoutRL，一种基于强化学习的端到端框架，通过优化归一化编辑距离、段落数量准确率和阅读顺序保持的综合奖励函数，使模型具备显式版式感知能力。基于新发布的Infinity-Doc-55K数据集（含55,000张高保真合成扫描文档与专家筛选的真实文档），我们构建了Infinity-Parser，一个基于视觉-语言模型的解析器。在英文和中文的OCR、表格与公式提取及阅读顺序检测基准上，Infinity-Parser在准确率与结构保真度上均超越专业流水线与通用视觉-语言模型，达到新SOTA。代码与数据集将公开，以推动鲁棒文档理解的发展。
原文摘要 · Abstract (English)
Automated parsing of scanned documents into richly structured, machine-readable formats remains a critical bottleneck in Document AI, as traditional multi-stage pipelines suffer from error propagation and limited adaptability to diverse layouts. We introduce layoutRL, an end-to-end reinforcement learning framework that trains models to be explicitly layout-aware by optimizing a composite reward of normalized edit distance, paragraph count accuracy, and reading order preservation. Leveraging our newly released dataset, Infinity-Doc-55K, which combines 55K high-fidelity synthetic scanned document parsing data with expert-filtered real-world documents, we instantiate layoutRL in a vision-language-model-based parser called Infinity-Parser. Evaluated on English and Chinese benchmarks for OCR, table and formula extraction, and reading order detection, Infinity-Parser achieves new state-of-the-art performance in both accuracy and structural fidelity, outpacing specialist pipelines and general-purpose vision-language models. We will publicly release our code and dataset to accelerate progress in robust document understanding.
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