💻 Github | 📊 Dataset (coming soon...) | 📄 Paper (coming soon...) | 🚀 Demo (coming soon...)
# News - [2026-04-09] We released our latest flagship document parsing model, Infinity-Parser2-Pro. Note that this is still a preview version. Model weights are being uploaded. # Introduction 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-1.5, 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. ## Key Features - 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-1.5, 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. # Performance Coming soon... # Citation Coming soon... # License This model is licensed under apache-2.0.