---
title: "Infinity-Parser — paper by Wang et al. (2026)"
description: "On the training side, they propose Layout-Aware RL (LayoutRL), which optimizes a vision-language model with a composite… 59 references."
authors: ["Baode Wang", "Biao Wu", "Weizhen Li", "Meng Fang", "Zuming Huang", "Jun Huang", "Yanjie Liang", "Haozhe Wang", "Ling Chen", "Wei Chu", "Yuan Qi"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.82/"
doi: "10.18653/v1/2026.findings-acl.82"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-infinity-parser-layout-aware-reinforcement/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2026-infinity-parser-layout-aware-reinforcement/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/) / Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset

# Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset

**Authors:** Baode Wang, [Biao Wu](https://md.research.elamur.ai/free/en/authors/biao-wu/), Weizhen Li, [Meng Fang](https://md.research.elamur.ai/free/en/authors/meng-fang/), Zuming Huang, [Jun Huang](https://md.research.elamur.ai/free/en/authors/jun-huang/), Yanjie Liang, [Haozhe Wang](https://md.research.elamur.ai/free/en/authors/haozhe-wang/), [Ling Chen](https://md.research.elamur.ai/free/en/authors/ling-chen/), [Wei Chu](https://md.research.elamur.ai/free/en/authors/wei-chu/) and [Yuan Qi](https://md.research.elamur.ai/free/en/authors/yuan-qi/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.82/) · [PDF](https://aclanthology.org/2026.findings-acl.82.pdf) · [DOI 10.18653/v1/2026.findings-acl.82](https://doi.org/10.18653/v1/2026.findings-acl.82) · [OpenAlex](https://openalex.org/W7166803669)

**Citations:** 4 citations (OpenAlex, 2026-10-14)

## Summary

Converting scanned pages into structured Markdown is difficult. Pages mix paragraphs, tables, formulas and figures, and a parser must recover both the content and its layout and reading order. The authors identify two obstacles: there is little training data with precise, layout-aware annotation, and supervised fine-tuning (SFT) tends to learn surface patterns that do not transfer to unfamiliar document types.

To address the data problem, they build Infinity-Doc-400K, which combines two sources:
- **Synthetic pages**, produced by filling HTML templates and rendering them in a browser, so the labels match the image exactly.
- **Real scanned documents**, labelled by several expert models whose outputs are cross-checked and then refined with rules.

On the training side, they propose Layout-Aware RL (LayoutRL), which optimizes a vision-language model with a composite, verifiable reward. The reward combines edit distance, paragraph-count agreement and reading-order correctness.

The resulting model, Infinity-Parser, reports state-of-the-art results on OmniDocBench, olmOCR-Bench, PubTabNet and FinTabNet. It beats both pipeline systems and general VLMs, with the largest gains appearing on out-of-distribution domains. This suggests reward-based training is a practical route to more robust document parsing.

## Key points

- **Dataset:** Infinity-Doc-400K contains 400,066 scanned documents paired with ground-truth Markdown. Real sources include financial reports, medical records, papers, books, magazines and web pages.
- **Reward design:** The RL reward has three parts: an edit distance normalized by length, a score for getting the number of paragraphs right, and a term for keeping the correct reading order. No explicit chain-of-thought reasoning is involved.
- **Ablation on data type:** RL on the combined data (43K) reaches an overall edit distance of 0.122 (EN) and 0.174 (ZH). Synthetic-only RL is clearly worse, scoring 0.182/0.224 at 43K and 0.166/0.198 at 69K. The authors conclude that layout diversity is essential.
- **SFT vs. RL:** At an equal 5k scale, synthetic data matches or beats real data under SFT (edit 0.290 vs. 0.337). RL improves page-level, layout-aware accuracy, while SFT mainly helps paragraph-level accuracy. RL's advantage is largest on unseen domains such as textbooks and slides.
- **Limitation:** The method was not tested on much larger models, such as 32B or 70B parameters. The authors plan to release code, data and the model.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works

## Cite

Cite as: Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu and Yuan Qi. 2026. Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2026infinity,
  title = {{Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset}},
  author = {Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu and Yuan Qi},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.82},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.82}
}
```

- [← previous InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-infiniteweb-scalable-web-environment-synthesis/)
- [next → Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design](https://md.research.elamur.ai/free/en/papers/yao-2026-instructional-agents-reducing-teaching-faculty/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/)

## References

59 references, 24 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv:2303.08774.
2. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2024. [Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/). arXiv:2308.12966.
3. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, and 1 others. 2025a. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923. — [Qwen2.5-VL Technical Report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
4. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, and 1 others. 2025b. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923. — [Qwen2.5-VL Technical Report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
5. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and 1 others. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in neural information processing systems, 33:1877–1901.
6. Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, Bin Li, Ping Luo, Tong Lu, Yu Qiao, and Jifeng Dai. 2024. [Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/). In Proceedings of the IEEE/CV…
7. Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V Le, Sergey Levine, and Yi Ma. 2025. [Sft memorizes, rl generalizes: A comparative study of foundation model post-training](https://md.research.elamur.ai/free/en/papers/chu-2025-sft-memorizes-rl-generalizes-comparative/). arXiv preprint arXiv:2501.17161.
8. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, and 1 others. 2020. [An image is worth 16x16 words: Transformers for image recognition at scale](https://md.research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/). arXiv preprint arXiv:2010.1192…
9. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. [Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/). arXiv preprint arXiv:2501.12948.
10. Daniil Gurgurov and Aleksey Morshnev. 2024. Image-to-latex converter for mathematical formulas and text. arXiv preprint arXiv:2408.04015. [find](https://scholar.google.com/scholar?q=Daniil+Gurgurov+and+Aleksey+Morshnev%2E+2024%2E+Image%2Dto%2Dlatex+converter+for+mathematical+formulas+and+text%2E+arXiv+preprint+arXiv%3A2408%2E04015%2E)
11. Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Chen Li, Ji Zhang, Qin Jin, Fei Huang, and 1 others. 2024a. [mplug-docowl 1.5: Unified structure learning for ocr-free document understanding](https://md.research.elamur.ai/free/en/papers/hu-2024-mplug-docowl-1-5-unified/). arXiv preprint arXiv:2403.12895.
12. Anwen Hu, Haiyang Xu, Liang Zhang, Jiabo Ye, Ming Yan, Ji Zhang, Qin Jin, Fei Huang, and Jingren Zhou. 2024b. [mplug-docowl2: High-resolution compressing for ocr-free multi-page document understanding](https://md.research.elamur.ai/free/en/papers/hu-2025-mplug-docowl2-high-resolution-compressing/). arXiv preprint arXiv:2409.03420.
13. Lukas Blecher, Guillem Cucurull, Thomas Scialom, and Robert Stojnic. 2023. Nougat: Neural optical understanding for academic documents. arXiv preprint arXiv:2308.13418. [find](https://scholar.google.com/scholar?q=Lukas+Blecher%2C+Guillem+Cucurull%2C+Thomas+Scialom%2C+and+Robert+Stojnic%2E+2023%2E+Nougat%3A+Neural+optical+understanding+for+academic+documents%2E+arXiv+preprint+arXiv%3A2308%2E13418%2E)
14. Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Yao Hu, and Shaohui Lin. 2025. Vision-r1: Incentivizing reasoning capability in multimodal large language models. arXiv preprint arXiv:2503.06749. [find](https://scholar.google.com/scholar?q=Wenxuan+Huang%2C+Bohan+Jia%2C+Zijie+Zhai%2C+Shaosheng+Cao%2C+Zheyu+Ye%2C+Fei+Zhao%2C+Yao+Hu%2C+and+Shaohui+Lin%2E+2025%2E+Vision%2Dr1%3A+Incentivizing+reasoning+capability+in+multimodal+large+language+models%2E+arXiv+preprin)
15. Lukas Blecher, Guillem Cucurull, Thomas Scialom, and Robert Stojnic. 2024. Nougat: Neural optical understanding for academic documents. arXiv:2308.13418. [find](https://scholar.google.com/scholar?q=Lukas+Blecher%2C+Guillem+Cucurull%2C+Thomas+Scialom%2C+and+Robert+Stojnic%2E+2024%2E+Nougat%3A+Neural+optical+understanding+for+academic+documents%2E+arXiv%3A2308%2E13418%2E)
16. Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, and Furu Wei. 2022. [Layoutlmv3: Pre-training for document ai with unified text and image masking](https://md.research.elamur.ai/free/en/papers/huang-2022-layoutlmv3-pre-training-document-ai/). Preprint, arXiv:2204.08387.
17. Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Sohee Yang, and Minjoon Seo. 2021. Spatial dependency parsing for semi-structured document information extraction. In Findings of the Association for Computational Linguistics: ACL-IJCNLP, pages 330–343. Association for Computational Linguistics (ACL). [find](https://scholar.google.com/scholar?q=Wonseok+Hwang%2C+Jinyeong+Yim%2C+Seunghyun+Park%2C+Sohee+Yang%2C+and+Minjoon+Seo%2E+2021%2E+Spatial+dependency+parsing+for+semi%2Dstructured+document+information+extraction%2E+In+Findings+of+the+Association+for+Compu)
18. Harold W Kuhn. 1955. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83–97. [find](https://scholar.google.com/scholar?q=Harold+W+Kuhn%2E+1955%2E+The+hungarian+method+for+the+assignment+problem%2E+Naval+research+logistics+quarterly%2C+2%281%2D2%29%3A83%E2%80%9397%2E)
19. Chenxia Li, Weiwei Liu, Ruoyu Guo, Xiaoting Yin, Kaitao Jiang, Yongkun Du, Yuning Du, Lingfeng Zhu, Baohua Lai, Xiaoguang Hu, Dianhai Yu, and Yanjun Ma. 2022a. Pp-ocrv3: More attempts for the improvement of ultra lightweight ocr system. Preprint, arXiv:2206.03001. — [arXiv:2206.03001](https://arxiv.org/abs/2206.03001) [find](https://scholar.google.com/scholar?q=Chenxia+Li%2C+Weiwei+Liu%2C+Ruoyu+Guo%2C+Xiaoting+Yin%2C+Kaitao+Jiang%2C+Yongkun+Du%2C+Yuning+Du%2C+Lingfeng+Zhu%2C+Baohua+Lai%2C+Xiaoguang+Hu%2C+Dianhai+Yu%2C+and+Yanjun+Ma%2E+2022a%2E+Pp%2Docrv3%3A+More+attempts+for+the+improvem)
20. Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, and 1 others. 2022b. [Competition-level code generation with alphacode](https://md.research.elamur.ai/free/en/papers/li-2022-competition-level-code-generation-alphacode/). Science, 378(6624):1092–1097.
21. Zhang Li, Yuliang Liu, Qiang Liu, Zhiyin Ma, Ziyang Zhang, Shuo Zhang, Zidun Guo, Jiarui Zhang, Xinyu Wang, and Xiang Bai. 2025. Monkeyocr: Document parsing with a structure-recognition-relation triplet paradigm. arXiv preprint arXiv:2506.05218. [find](https://scholar.google.com/scholar?q=Zhang+Li%2C+Yuliang+Liu%2C+Qiang+Liu%2C+Zhiyin+Ma%2C+Ziyang+Zhang%2C+Shuo+Zhang%2C+Zidun+Guo%2C+Jiarui+Zhang%2C+Xinyu+Wang%2C+and+Xiang+Bai%2E+2025%2E+Monkeyocr%3A+Document+parsing+with+a+structure%2Drecognition%2Drelation+tripl)
22. Chenglong Liu, Haoran Wei, Jinyue Chen, Lingyu Kong, Zheng Ge, Zining Zhu, Liang Zhao, Jianjian Sun, Chunrui Han, and Xiangyu Zhang. 2024. Focus anywhere for fine-grained multi-page document understanding. arXiv:2405.14295. — [arXiv:2405.14295](https://arxiv.org/abs/2405.14295) [find](https://scholar.google.com/scholar?q=Chenglong+Liu%2C+Haoran+Wei%2C+Jinyue+Chen%2C+Lingyu+Kong%2C+Zheng+Ge%2C+Zining+Zhu%2C+Liang+Zhao%2C+Jianjian+Sun%2C+Chunrui+Han%2C+and+Xiangyu+Zhang%2E+2024%2E+Focus+anywhere+for+fine%2Dgrained+multi%2Dpage+document+understan)
23. Yuqi Liu, Tianyuan Qu, Zhisheng Zhong, Bohao Peng, Shu Liu, Bei Yu, and Jiaya Jia. 2025a. Visionreasoner: Unified visual perception and reasoning via reinforcement learning. arXiv preprint arXiv:2505.12081. — [arXiv:2505.12081](https://arxiv.org/abs/2505.12081) [find](https://scholar.google.com/scholar?q=Yuqi+Liu%2C+Tianyuan+Qu%2C+Zhisheng+Zhong%2C+Bohao+Peng%2C+Shu+Liu%2C+Bei+Yu%2C+and+Jiaya+Jia%2E+2025a%2E+Visionreasoner%3A+Unified+visual+perception+and+reasoning+via+reinforcement+learning%2E+arXiv+preprint+arXiv%3A2505%2E)
24. Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, and Jiaqi Wang. 2025b. Visual-rft: Visual reinforcement fine-tuning. arXiv preprint arXiv:2503.01785. — [arXiv:2503.01785](https://arxiv.org/abs/2503.01785) [find](https://scholar.google.com/scholar?q=Ziyu+Liu%2C+Zeyi+Sun%2C+Yuhang+Zang%2C+Xiaoyi+Dong%2C+Yuhang+Cao%2C+Haodong+Duan%2C+Dahua+Lin%2C+and+Jiaqi+Wang%2E+2025b%2E+Visual%2Drft%3A+Visual+reinforcement+fine%2Dtuning%2E+arXiv+preprint+arXiv%3A2503%2E01785%2E+%E2%80%94+%5BarXiv%3A2503%2E0)
25. Nikolaos Livathinos, Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi, Panos Vagenas, Cesar Berrospi Ramis, Matteo Omenetti, Kasper Dinkla, Yusik Kim, and 1 others. 2025. Docling: An efficient open-source toolkit for ai-driven document conversion. arXiv preprint arXiv:2501.17887. — [arXiv:2501.17887](https://arxiv.org/abs/2501.17887) [find](https://scholar.google.com/scholar?q=Nikolaos+Livathinos%2C+Christoph+Auer%2C+Maksym+Lysak%2C+Ahmed+Nassar%2C+Michele+Dolfi%2C+Panos+Vagenas%2C+Cesar+Berrospi+Ramis%2C+Matteo+Omenetti%2C+Kasper+Dinkla%2C+Yusik+Kim%2C+and+1+others%2E+2025%2E+Docling%3A+An+efficien)
26. Tengchao Lv, Yupan Huang, Jingye Chen, Yuzhong Zhao, Yilin Jia, Lei Cui, Shuming Ma, Yaoyao Chang, Shaohan Huang, Wenhui Wang, Li Dong, Weiyao Luo, Shaoxiang Wu, Guoxin Wang, Cha Zhang, and Furu Wei. 2024. Kosmos-2.5: A multimodal literate model. Preprint, arXiv:2309.11419. — [arXiv:2309.11419](https://arxiv.org/abs/2309.11419) [find](https://scholar.google.com/scholar?q=Tengchao+Lv%2C+Yupan+Huang%2C+Jingye+Chen%2C+Yuzhong+Zhao%2C+Yilin+Jia%2C+Lei+Cui%2C+Shuming+Ma%2C+Yaoyao+Chang%2C+Shaohan+Huang%2C+Wenhui+Wang%2C+Li+Dong%2C+Weiyao+Luo%2C+Shaoxiang+Wu%2C+Guoxin+Wang%2C+Cha+Zhang%2C+and+Furu+Wei%2E+)
27. AI Meta. 2024. Llama 3.2: Revolutionizing edge ai and vision with open, customizable models. Meta AI Blog. Retrieved December, 20:2024. [find](https://scholar.google.com/scholar?q=AI+Meta%2E+2024%2E+Llama+3%2E2%3A+Revolutionizing+edge+ai+and+vision+with+open%2C+customizable+models%2E+Meta+AI+Blog%2E+Retrieved+December%2C+20%3A2024%2E)
28. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, and 1 others. 2021. [Webgpt: Browser-assisted question-answering with human feedback](https://md.research.elamur.ai/free/en/papers/nakano-2021-webgpt-browser-assisted-question-answering/). arXiv preprint arXiv:2112.09332.
29. Tobias Nipkow. 2003. Jinja: Towards a comprehensive formal semantics for a java-like language. In Proc. Marktobderdorf Summer School. IOS Press Amsterdam. [find](https://scholar.google.com/scholar?q=Tobias+Nipkow%2E+2003%2E+Jinja%3A+Towards+a+comprehensive+formal+semantics+for+a+java%2Dlike+language%2E+In+Proc%2E+Marktobderdorf+Summer+School%2E+IOS+Press+Amsterdam%2E)
30. OpenAI. 2024. [Gpt-4o system card](https://md.research.elamur.ai/free/en/papers/openai-2024-gpt-4o-system-card/). arXiv:2410.21276.
31. Preprint, Linke Ouyang, Yuan Qu, Hongbin Zhou, Jiawei Zhu, Rui Zhang, Qunshu Lin, Bin Wang, Zhiyuan Zhao, Man Jiang, Xiaomeng Zhao, and 1 others. 2024. Omnidocbench: Benchmarking diverse pdf document parsing with comprehensive annotations. arXiv preprint arXiv:2412.07626. — [arXiv:2412.07626](https://arxiv.org/abs/2412.07626) [find](https://scholar.google.com/scholar?q=Preprint%2C+Linke+Ouyang%2C+Yuan+Qu%2C+Hongbin+Zhou%2C+Jiawei+Zhu%2C+Rui+Zhang%2C+Qunshu+Lin%2C+Bin+Wang%2C+Zhiyuan+Zhao%2C+Man+Jiang%2C+Xiaomeng+Zhao%2C+and+1+others%2E+2024%2E+Omnidocbench%3A+Benchmarking+diverse+pdf+document+)
32. Vik Paruchuri. 2024. Marker. [find](https://scholar.google.com/scholar?q=Vik+Paruchuri%2E+2024%2E+Marker%2E)
33. Yi Peng, Xiaokun Wang, Yichen Wei, Jiangbo Pei, Weijie Qiu, Ai Jian, Yunzhuo Hao, Jiachun Pan, Tianyidan Xie, Li Ge, and 1 others. 2025. [Skywork r1v: pioneering multimodal reasoning with chain-of-thought](https://md.research.elamur.ai/free/en/papers/peng-2025-skywork-r1v-pioneering-multimodal/). arXiv preprint arXiv:2504.05599.
34. Jake Poznanski, Jon Borchardt, Jason Dunkelberger, Regan Huff, Daniel Lin, Aman Rangapur, Christopher Wilhelm, Kyle Lo, and Luca Soldaini. 2025. [olmocr: Unlocking trillions of tokens in pdfs with vision language models](https://md.research.elamur.ai/free/en/papers/poznanski-2025-olmocr-unlocking-trillions-tokens-pdfs/). arXiv preprint arXiv:2502.18443.
35. RapidAI. 2023. Rapidtable. [https://github.com/](https://github.com/) RapidAI/RapidTable. [↗](https://github.com/)
36. Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. [Toolformer: Language models can teach themselves to use tools](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/). Advances in Neural Information Processing Systems, 36:68539–68551.
37. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, and 1 others. 2024. [Deepseekmath: Pushing the limits of mathematical reasoning in open language models](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/). arXiv preprint arXiv:2402.03300.
38. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. 2024. [Hybridflow: A flexible and efficient rlhf framework](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/). arXiv preprint arXiv: 2409.19256.
39. Alex Su, Haozhe Wang, Weimin Ren, Fangzhen Lin, and Wenhu Chen. 2025. Pixel reasoner: Incentivizing pixel-space reasoning with curiosity-driven reinforcement learning. arXiv preprint arXiv:2505.15966. — [arXiv:2505.15966](https://arxiv.org/abs/2505.15966) [find](https://scholar.google.com/scholar?q=Alex+Su%2C+Haozhe+Wang%2C+Weimin+Ren%2C+Fangzhen+Lin%2C+and+Wenhu+Chen%2E+2025%2E+Pixel+reasoner%3A+Incentivizing+pixel%2Dspace+reasoning+with+curiosity%2Ddriven+reinforcement+learning%2E+arXiv+preprint+arXiv%3A2505%2E15966%2E)
40. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, and 1 others. 2023. [Gemini: a family of highly capable multimodal models](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/). arXiv preprint arXiv:2312.11805.
41. Bin Wang, Zhuangcheng Gu, Guang Liang, Chao Xu, Bo Zhang, Botian Shi, and Conghui He. 2024a. Unimernet: A universal network for real-world mathematical expression recognition. Preprint, arXiv:2404.15254. [find](https://scholar.google.com/scholar?q=Bin+Wang%2C+Zhuangcheng+Gu%2C+Guang+Liang%2C+Chao+Xu%2C+Bo+Zhang%2C+Botian+Shi%2C+and+Conghui+He%2E+2024a%2E+Unimernet%3A+A+universal+network+for+real%2Dworld+mathematical+expression+recognition%2E+Preprint%2C+arXiv%3A2404%2E152)
42. Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, Bo Zhang, Liqun Wei, Zhihao Sui, Wei Li, Botian Shi, Yu Qiao, Dahua Lin, and Conghui He. 2024b. Mineru: An open-source solution for precise document content extraction. arXiv:2409.18839. [find](https://scholar.google.com/scholar?q=Bin+Wang%2C+Chao+Xu%2C+Xiaomeng+Zhao%2C+Linke+Ouyang%2C+Fan+Wu%2C+Zhiyuan+Zhao%2C+Rui+Xu%2C+Kaiwen+Liu%2C+Yuan+Qu%2C+Fukai+Shang%2C+Bo+Zhang%2C+Liqun+Wei%2C+Zhihao+Sui%2C+Wei+Li%2C+Botian+Shi%2C+Yu+Qiao%2C+Dahua+Lin%2C+and+Conghui+He%2E)
43. Haozhe Wang, Long Li, Chao Qu, Fengming Zhu, Weidi Xu, Wei Chu, and Fangzhen Lin. 2025a. To code or not to code? adaptive tool integration for math language models via expectation-maximization. arXiv preprint arXiv:2502.00691. [find](https://scholar.google.com/scholar?q=Haozhe+Wang%2C+Long+Li%2C+Chao+Qu%2C+Fengming+Zhu%2C+Weidi+Xu%2C+Wei+Chu%2C+and+Fangzhen+Lin%2E+2025a%2E+To+code+or+not+to+code%3F+adaptive+tool+integration+for+math+language+models+via+expectation%2Dmaximization%2E+arXiv+)
44. Haozhe Wang, Chao Qu, Zuming Huang, Wei Chu, Fangzhen Lin, and Wenhu Chen. 2025b. Vl-rethinker: Incentivizing self-reflection of vision-language models with reinforcement learning. arXiv preprint arXiv:2504.08837. [find](https://scholar.google.com/scholar?q=Haozhe+Wang%2C+Chao+Qu%2C+Zuming+Huang%2C+Wei+Chu%2C+Fangzhen+Lin%2C+and+Wenhu+Chen%2E+2025b%2E+Vl%2Drethinker%3A+Incentivizing+self%2Dreflection+of+vision%2Dlanguage+models+with+reinforcement+learning%2E+arXiv+preprint+arXi)
45. Haozhe Wang, Chao Qu, Zuming Huang, Wei Chu, Fangzhen Lin, and Wenhu Chen. 2025c. Vl-rethinker: Incentivizing self-reflection of vision-language models with reinforcement learning. arXiv preprint arXiv:2504.08837. [find](https://scholar.google.com/scholar?q=Haozhe+Wang%2C+Chao+Qu%2C+Zuming+Huang%2C+Wei+Chu%2C+Fangzhen+Lin%2C+and+Wenhu+Chen%2E+2025c%2E+Vl%2Drethinker%3A+Incentivizing+self%2Dreflection+of+vision%2Dlanguage+models+with+reinforcement+learning%2E+arXiv+preprint+arXi)
46. Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, and 1 others. 2024c. [Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution](https://md.research.elamur.ai/free/en/papers/wang-2024-qwen2-vl-enhancing-vision/). arXiv preprint arXiv:2409.12191.
47. Haoran Wei, Lingyu Kong, Jinyue Chen, Liang Zhao, Zheng Ge, Jinrong Yang, Jianjian Sun, Chunrui Han, and Xiangyu Zhang. 2025a. Vary: Scaling up the vision vocabulary for large vision-language model. In European Conference on Computer Vision, pages 408–424. Springer. [find](https://scholar.google.com/scholar?q=Haoran+Wei%2C+Lingyu+Kong%2C+Jinyue+Chen%2C+Liang+Zhao%2C+Zheng+Ge%2C+Jinrong+Yang%2C+Jianjian+Sun%2C+Chunrui+Han%2C+and+Xiangyu+Zhang%2E+2025a%2E+Vary%3A+Scaling+up+the+vision+vocabulary+for+large+vision%2Dlanguage+model%2E+I)
48. Haoran Wei, Chenglong Liu, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, Zheng Ge, Liang Zhao, Jianjian Sun, Yuang Peng, and 1 others. 2024. General ocr theory: Towards ocr-2.0 via a unified end-to-end model. arXiv:2409.01704. — [arXiv:2409.01704](https://arxiv.org/abs/2409.01704) [find](https://scholar.google.com/scholar?q=Haoran+Wei%2C+Chenglong+Liu%2C+Jinyue+Chen%2C+Jia+Wang%2C+Lingyu+Kong%2C+Yanming+Xu%2C+Zheng+Ge%2C+Liang+Zhao%2C+Jianjian+Sun%2C+Yuang+Peng%2C+and+1+others%2E+2024%2E+General+ocr+theory%3A+Towards+ocr%2D2%2E0+via+a+unified+end%2Dto%2D)
49. Haoran Wei, Yaofeng Sun, and Yukun Li. 2025b. [Deepseek-ocr: Contexts optical compression](https://md.research.elamur.ai/free/en/papers/wei-2025-deepseek-ocr-contexts-optical/). arXiv preprint arXiv:2510.18234.
50. Renqiu Xia, Song Mao, Xiangchao Yan, Hongbin Zhou, Bo Zhang, Haoyang Peng, Jiahao Pi, Daocheng Fu, Wenjie Wu, Hancheng Ye, and 1 others. 2024. [find](https://scholar.google.com/scholar?q=Renqiu+Xia%2C+Song+Mao%2C+Xiangchao+Yan%2C+Hongbin+Zhou%2C+Bo+Zhang%2C+Haoyang+Peng%2C+Jiahao+Pi%2C+Daocheng+Fu%2C+Wenjie+Wu%2C+Hancheng+Ye%2C+and+1+others%2E+2024%2E)
51. Docgenome: An open large-scale scientific document benchmark for training and testing multimodal large language models. arXiv preprint arXiv:2406.11633. — [arXiv:2406.11633](https://arxiv.org/abs/2406.11633) [find](https://scholar.google.com/scholar?q=Docgenome%3A+An+open+large%2Dscale+scientific+document+benchmark+for+training+and+testing+multimodal+large+language+models%2E+arXiv+preprint+arXiv%3A2406%2E11633%2E+%E2%80%94+%5BarXiv%3A2406%2E11633%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2406)
52. Junting Lu Yaowei Zheng. 2025. Easyr1: An efficient, scalable, multi-modality rl training framework. [https://github.com/hiyouga/EasyR1](https://github.com/hiyouga/EasyR1). [↗](https://github.com/hiyouga/EasyR1)
53. Huaye Zeng, Dongfu Jiang, Haozhe Wang, Ping Nie, Xiaotong Chen, and Wenhu Chen. 2025. Acecoder: Acing coder rl via automated test-case synthesis. arXiv preprint arXiv:2502.01718. [find](https://scholar.google.com/scholar?q=Huaye+Zeng%2C+Dongfu+Jiang%2C+Haozhe+Wang%2C+Ping+Nie%2C+Xiaotong+Chen%2C+and+Wenhu+Chen%2E+2025%2E+Acecoder%3A+Acing+coder+rl+via+automated+test%2Dcase+synthesis%2E+arXiv+preprint+arXiv%3A2502%2E01718%2E)
54. Qintong Zhang, Victor Shea-Jay Huang, Bin Wang, Junyuan Zhang, Zhengren Wang, Hao Liang, Shawn Wang, Matthieu Lin, Wentao Zhang, and Conghui He. 2024. Document parsing unveiled: Techniques, challenges, and prospects for structured information extraction. arXiv preprint arXiv:2410.21169. — [arXiv:2410.21169](https://arxiv.org/abs/2410.21169) [find](https://scholar.google.com/scholar?q=Qintong+Zhang%2C+Victor+Shea%2DJay+Huang%2C+Bin+Wang%2C+Junyuan+Zhang%2C+Zhengren+Wang%2C+Hao+Liang%2C+Shawn+Wang%2C+Matthieu+Lin%2C+Wentao+Zhang%2C+and+Conghui+He%2E+2024%2E+Document+parsing+unveiled%3A+Techniques%2C+challenges)
55. Xinyi Zheng, Douglas Burdick, Lucian Popa, Xu Zhong, and Nancy Xin Ru Wang. 2021. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 6… [find](https://scholar.google.com/scholar?q=Xinyi+Zheng%2C+Douglas+Burdick%2C+Lucian+Popa%2C+Xu+Zhong%2C+and+Nancy+Xin+Ru+Wang%2E+2021%2E+Global+table+extractor+%28gte%29%3A+A+framework+for+joint+table+identification+and+cell+structure+recognition+using+visual+c)
56. Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. 2020a. Image-based table recognition: data, model, and evaluation. In European conference on computer vision, pages 564–580. [find](https://scholar.google.com/scholar?q=Xu+Zhong%2C+Elaheh+ShafieiBavani%2C+and+Antonio+Jimeno+Yepes%2E+2020a%2E+Image%2Dbased+table+recognition%3A+data%2C+model%2C+and+evaluation%2E+In+European+conference+on+computer+vision%2C+pages+564%E2%80%93580%2E)
57. Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. 2020b. Image-based table recognition: data, model, and evaluation. In European conference on computer vision, pages 564–580. Springer. [find](https://scholar.google.com/scholar?q=Xu+Zhong%2C+Elaheh+ShafieiBavani%2C+and+Antonio+Jimeno+Yepes%2E+2020b%2E+Image%2Dbased+table+recognition%3A+data%2C+model%2C+and+evaluation%2E+In+European+conference+on+computer+vision%2C+pages+564%E2%80%93580%2E+Springer%2E)
58. Hongbin Zhou, Xiangchao Yan, and Bo Zhang. 2024. Structeqtable-deploy: A high-efficiency open-source toolkit for table-to-latex transformation. [https://github.com/UniModal4Reasoning/](https://github.com/UniModal4Reasoning/) StructEqTable-Deploy. [↗](https://github.com/UniModal4Reasoning/)
59. Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Yuchen Duan, Hao Tian, Weijie Su, Jie Shao, and 1 others. 2025. [Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models](https://md.research.elamur.ai/free/en/papers/zhu-2025-internvl3-exploring-advanced-training/). arXiv preprint arXiv:2504.10479.

*Updated 1 November 2026*