Infinity-Parser2 Technical Report
Infinity-Parser2 introduces a novel architecture combining a controllable data-synthesis engine with multi-task reinforcement learning to solve the scarcity of high-quality document parsing data. The team released Infinity-Doc2-5M, a 5-million-sample bilingual corpus featuring precise annotations for bounding boxes, reading order, and various content formats like Markdown, LaTeX, and chemical structures. A verifiable, multi-task reward system enables Joint Reinforcement Learning across eight dis
Analysis
TL;DR
- Infinity-Parser2 introduces a novel architecture combining a controllable data-synthesis engine with multi-task reinforcement learning to solve the scarcity of high-quality document parsing data.
- The team released Infinity-Doc2-5M, a 5-million-sample bilingual corpus featuring precise annotations for bounding boxes, reading order, and various content formats like Markdown, LaTeX, and chemical structures.
- A verifiable, multi-task reward system enables Joint Reinforcement Learning across eight distinct objectives, unifying perception, structural analysis, and reasoning into a single optimization signal.
- Two model variants are available: Infinity-Parser2-Flash for high-throughput, low-latency inference (3.68x faster than its predecessor) and Infinity-Parser2-Pro for maximum precision.
- Infinity-Parser2-Pro achieves state-of-the-art results on olmOCR-Bench (87.6%) and ParseBench (74.3%), outperforming competitors like DeepSeek-OCR-2 and MinerU2.5, particularly in complex domains like charts and chemical formulas.
Why It Matters
This development addresses a critical bottleneck in document AI: the lack of faithful, large-scale annotated data. By automating data synthesis and leveraging reinforcement learning for multi-task alignment, Infinity-Parser2 offers a scalable path to high-precision document understanding without relying solely on manual annotation. For practitioners, the availability of both a speed-optimized and precision-optimized variant allows for flexible deployment strategies depending on latency versus accuracy requirements in production environments.
Technical Details
- Data Synthesis Engine: Utilizes a controllable rendering framework with an iterative refinement loop to generate synthetic training data, resulting in the open-source Infinity-Doc2-5M corpus. This dataset includes bilingual (Chinese/English) samples with detailed annotations for layout, reading order, and specific content types (Markdown, HTML, LaTeX, SMILES, structured charts).
- Multi-Task Reinforcement Learning: Implements a verifiable reward system that supports 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.
- Model Variants:
- Infinity-Parser2-Flash: Optimized for inference speed, delivering a 3.68x throughput increase compared to Infinity-Parser-7B.
- Infinity-Parser2-Pro: Designed for precision-critical applications, focusing on accuracy across diverse document structures.
- Performance Benchmarks: The Pro variant sets new standards with 87.6% accuracy on olmOCR-Bench and 74.3% on ParseBench, demonstrating superior generalization to specialized domains such as chemical formulas and complex charts.
Industry Insight
The shift towards synthetic data generation coupled with reinforcement learning signals a move away from expensive, manual annotation pipelines for specialized vision-language tasks. Organizations should consider adopting similar controllable synthesis frameworks to scale their document processing capabilities. Furthermore, the dual-release strategy of Flash and Pro models highlights the importance of tailoring model selection to specific operational constraints, suggesting that future enterprise deployments may require hybrid approaches or dynamic routing between speed and precision models.
Disclaimer: The above content is generated by AI and is for reference only.