PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training
Manhui Lin Yue Zhang Zelun Zhang Hongen Liu Suyin Liang Yubo Zhang Yiqing Xiang Jiaxuan Liu Ting Sun Changda Zhou Tingquan Gao Cheng Cui Yi Liu Dianhai Yu Yanjun Ma
作者信息
Abstract
We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. Although PaddleOCR-VL-1.5 establishes a strong 0.9B baseline, its remaining errors concentrate in under-optimized regions where model behavior is unstable, data coverage is sparse, or supervision is unreliable. Rather than expanding the training corpus indiscriminately, PaddleOCR-VL-1.6 introduces a region-aware data optimization framework that identifies weak regions from the previous model, applies targeted enhancement to these regions, and improves the reliability of supervision signals. It further adopts a progressive post-training recipe based on curated data selection and reinforcement learning, pushing model performance to a higher level through staged optimization. PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on OmniDocBench v1.6, demonstrates strong competitiveness against top-tier VLMs, and provides a practical post-training recipe for the PaddleOCR-VL series.引用本文复制引用
Manhui Lin,Yue Zhang,Zelun Zhang,Hongen Liu,Suyin Liang,Yubo Zhang,Yiqing Xiang,Jiaxuan Liu,Ting Sun,Changda Zhou,Tingquan Gao,Cheng Cui,Yi Liu,Dianhai Yu,Yanjun Ma.PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training[EB/OL].(2026-06-02)[2026-06-06].https://arxiv.org/abs/2606.03264.学科分类
计算技术、计算机技术
