Efficient Document Parsing via Parallel Token Prediction
Document parsing, as a fundamental yet crucial vision task, is being revolutionized by vision-language models (VLMs). However, the autoregressive (AR) decoding inherent to VLMs creates a significant bottleneck, severely limiting parsing speed. In this paper, we propose Parallel-Token Prediction (PTP), a plugable, model-agnostic and simple-yet-effective method that enables VLMs to generate multiple future tokens in parallel with improved sample efficiency. Specifically, we insert some learnable tokens into the input sequence and design corresponding training objectives to equip the model with parallel decoding capabilities for document parsing. Furthermore, to support effective training, we develop a comprehensive data generation pipeline that efficiently produces large-scale, high-quality document parsing training data for VLMs. Extensive experiments on OmniDocBench and olmOCR-bench demonstrate that our method not only significantly improves decoding speed (1.6x-2.2x) but also reduces model hallucinations and exhibits strong generalization abilities.
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Lei Li
admin_assigned
2026-03-17T08:49:39.965Z
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lilei
flow3rdown
user
flow3rdown
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Ze Zhao
admin_assigned
2026-03-17T08:49:49.855Z
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zezhao
zezhao123
user
zezhao123
69b90f506b3f36c249fbc615
Meng Li
69b90f506b3f36c249fbc616
Zhongwang Lun
69b90f506b3f36c249fbc617
Yi Yuan
69b90f506b3f36c249fbc618
Xingjing Lu
admin_assigned
2026-03-17T08:50:01.084Z
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xingjing lu
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Zheng Wei
admin_assigned
2026-03-17T08:50:14.874Z
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Frank Wei
Zhengwei-Frank
user
Zhengwei-Frank
69b90f506b3f36c249fbc61a
Jiang Bian
69b90f506b3f36c249fbc61b
Zang Li
Efficient Document Parsing via Parallel Token Prediction
Document parsing, as a fundamental yet crucial vision task, is being revolutionized by vision-language models (VLMs). However, the autoregressive (AR) decoding inherent to VLMs creates a significant bottleneck, severely limiting parsing speed. In this paper, we propose Parallel-Token Prediction (PTP), a plugable, model-agnostic and simple-yet-effective method that enables VLMs to generate multiple future tokens in parallel with improved sample efficiency. Specifically, we insert some learnable tokens into the input sequence and design corresponding training objectives to equip the model with parallel decoding capabilities for document parsing. Furthermore, to support effective training, we develop a comprehensive data generation pipeline that efficiently produces large-scale, high-quality document parsing training data for VLMs. Extensive experiments on OmniDocBench and olmOCR-bench demonstrate that our method not only significantly improves decoding speed (1.6x-2.2x) but also reduces model hallucinations and exhibits strong generalization abilities.