PaddleOCR-VL-1.6 hits 96.33% on OmniDocBench v1.6, paper says

[HF Papers] PaddleOCR-VL-1.6, an upgraded compact document parsing model built on PaddleOCR-VL-1.5, is presented in the paper "PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training" by Zelun Zhang and co-authors. According to the authors, PaddleOCR-VL-1.5 established a strong 0.9B baseline, but its remaining errors concentrate in under-optimized regions where model behavior is unstable, data coverage is sparse or supervision is unreliable. The paper says the new version avoids indiscriminate corpus expansion and instead introduces a region-aware data optimization framework that identifies weak regions from the previous model, applies targeted enhancement and improves supervision reliability, together with a progressive post-training recipe based on curated data selection and reinforcement learning. The authors report a new state-of-the-art score of 96.33% on OmniDocBench v1.6, strong competitiveness against top-tier VLMs and a practical post-training recipe for the PaddleOCR-VL series. The results come from a preprint and have not been peer-reviewed.