Infinity-Parser2 Technical Report
We present Infinity-Parser2, a large multimodal model that couples a controllable data-synthesis pipeline with multi-task reinforcement learning for end-to-end document parsing. It introduces Infinity-Doc2-5M, a 5-million-sample bilingual corpus, and a multi-task reward system for joint RL across eight objectives. Two variants are released: Flash (low-latency) and Pro (precision), with Pro achieving SOTA results on benchmarks.
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[Submitted on 8 Jul 2026]
Title:Infinity-Parser2 Technical Report
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Abstract:We present Infinity-Parser2, a large multimodal model that couples a controllable data-synthesis pipeline with multi-task reinforcement learning for end-to-end document parsing, addressing the persistent scarcity of faithfully annotated parsing corpora. Our contributions are threefold. First, we build a scalable synthesis engine, pairing a controllable rendering framework with an iterative refinement loop, and use it to construct and open-source Infinity-Doc2-5M: a 5-million-sample bilingual (Chinese/English) corpus spanning diverse document types, annotated with element bounding boxes, canonical content forms (Markdown, HTML, LaTeX, SMILES, structured charts), and full-page reading order. Second, we introduce a verifiable, multi-task reward system that enables Joint Reinforcement Learning across eight co-trained objectives (document parsing, layout analysis, table parsing, math formula parsing, chart parsing, chemical formula parsing, document VQA, and general multimodal understanding), unifying perception, structure, and reasoning in a single optimization signal. Third, we release two variants under a shared architecture: Infinity-Parser2-Flash, optimized for low-latency inference with a $3.68\times$ throughput gain over Infinity-Parser-7B, and Infinity-Parser2-Pro, engineered for precision-critical settings. Infinity-Parser2-Pro reaches state-of-the-art 87.6% on olmOCR-Bench and 74.3% on ParseBench, surpassing DeepSeek-OCR-2, PaddleOCR-VL-1.5, and MinerU2.5, with strong generalization to charts, chemical formulas, and document VQA.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.07836 [cs.AI]
(or arXiv:2607.07836v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.07836
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zuming Huang [view email] [v1] Wed, 8 Jul 2026 18:17:21 UTC (22,579 KB)
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