| # Infinity-Parser2-Pro |
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| <p align="center"> |
| <img src="assets/logo.png" width="400"/> |
| <p> |
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| <p align="center"> |
| 💻 <a href="https://github.com/infly-ai/INF-MLLM">Github</a> | |
| 📊 <a>Dataset (coming soon...)</a> | |
| 📄 <a>Paper (coming soon...)</a> | |
| 🚀 <a>Demo (coming soon...)</a> |
| </p> |
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| # News |
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| - [2026-04-11] We release Infinity-Parser2-Pro, our flagship document parsing model — now available as a preview. Stay tuned: the official release, the lightweight Infinity-Parser2-Flash, and our multimodal parsing dataset Infinity-Doc2-10M are coming soon. |
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| # Introduction |
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| We are excited to release Infinity-Parser2-Pro, our latest flagship document understanding model that achieves a new state-of-the-art on olmOCR-Bench with a score of 86.7%, surpassing frontier models such as DeepSeek-OCR-2, PaddleOCR-VL, and dots.mocr. Building on our previous model Infinity-Parser-7B, we have significantly enhanced our data engine and multi-task reinforcement learning approach. This enables the model to consolidate robust multi-modal parsing capabilities into a unified architecture, delivering brand-new zero-shot capabilities for diverse real-world business scenarios. |
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| ## Key Features |
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| - **Upgraded Data Engine**: We have comprehensively enhanced our synthetic data engine to support both fixed-layout and flexible-layout document formats. By generating over 1 million diverse full-text samples covering a wide range of document layouts, combined with a dynamic adaptive sampling strategy, we ensure highly balanced and robust multi-task learning across various document types. |
| - **Multi-Task Reinforcement Learning**: We designed a novel verifiable reward system to support Joint Reinforcement Learning (RL), enabling seamless and simultaneous co-optimization of multiple complex tasks, including doc2json and doc2markdown. |
| - **Breakthrough Parsing Performance**: It substantially outperforms our previous 7B model, achieving 86.7% on olmOCR-Bench, surpassing frontier models such as DeepSeek-OCR-2, PaddleOCR-VL, and dots.mocr. |
| - **Inference Acceleration**: By adopting the highly efficient MoE architecture, our inference throughput has increased by 21% (from 441 to 534 tokens/sec), reducing deployment latency and costs. |
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| # Performance |
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| <p align="left"> |
| <img src="assets/document_parsing_performance_evaluation.png" width="1200"/> |
| <p> |
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| # Quick Start |
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| Coming soon... |
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| # Citation |
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| Coming soon... |
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| # License |
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| This model is licensed under apache-2.0. |
