POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models
  for Document Conversion
Authors: name: Yuan Liu; name: Zhongyin Zhao; name: Le Tian; name: Haicheng Wang; name: Xubing Ye; name: Yangxiu You; name: Zilin Yu; name: Chuhan Wu; name: Xiao Zhou; name: Yang Yu; name: Jie Zhou
Published: 2025-09-01 07:54:18

Abstract: High-quality labeled data is essential for training accurate document
conversion models, particularly in domains with complex formats such as tables,
formulas, and multi-column text. However, manual annotation is both costly and
time-consuming, while automatic labeling using existing models often lacks
accuracy in handling such challenging scenarios. Consequently, training student
models by distilling outputs from teacher models can significantly limit their
performance in real-world applications. In this paper, we propose a fully
automated, distillation-free framework comprising two stages for constructing
high-quality document extraction datasets and models capable of handling
diverse document formats and layouts. In the first stage, we introduce a method
for generating large-scale, diverse synthetic data, which enables a model to
extract key elements in a unified format with strong initial performance. In
the second stage, we present a self-improvement approach that further adapts
the model, initially trained on synthetic data, to real-world documents.
Specifically, we first use the fine-tuned model to annotate real documents,
then apply a suite of filtering strategies to verify annotation quality, and
finally retrain the model on the verified dataset. By iteratively repeating
this process, we progressively enhance both the model's conversion capabilities
and the quality of the generated data. We train a public POINTS-1.5 model to
obtain POINTS-Reader, which surpasses many existing public and proprietary
models of comparable or larger size. Our model is available at
https://github.com/Tencent/POINTS-Reader.