{"ID":3005038,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-05T07:16:01.131756733Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.03264","arxiv_id":"2606.03264","title":"PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training","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.","short_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...","url_abs":"https://arxiv.org/abs/2606.03264","url_pdf":"https://arxiv.org/pdf/2606.03264v1","authors":"[\"Zelun Zhang\",\"Hongen Liu\",\"Suyin Liang\",\"Yubo Zhang\",\"Yiqing Xiang\",\"Jiaxuan Liu\",\"Ting Sun\",\"Manhui Lin\",\"Yue Zhang\",\"Changda Zhou\",\"Tingquan Gao\",\"Cheng Cui\",\"Yi Liu\",\"Dianhai Yu\",\"Yanjun Ma\"]","published":"2026-06-02T07:27:03Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}