DocR1: Evidence Page-Guided GRPO for Multi-Page Document Understanding

• URL: http://arxiv.org/abs/2508.07313v2
• Date: Fri, 29 Aug 2025 17:13:47 GMT
• Title: DocR1: Evidence Page-Guided GRPO for Multi-Page Document Understanding
• Authors: Junyu Xiong, Yonghui Wang, Weichao Zhao, Chenyu Liu, Bing Yin, Wengang Zhou, Houqiang Li,
• Abstract summary: We introduce DocR1, an MLLM trained with a novel RL framework, Evidence Page-Guided GRPO.<n>EviGRPO incorporates an evidence-aware reward mechanism that promotes a coarse-to-fine reasoning strategy.<n>We show that DocR1 achieves state-of-the-art performance on multi-page tasks, while consistently maintaining strong results on single-page benchmarks.
• Score: 100.29587871213624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding multi-page documents poses a significant challenge for multimodal large language models (MLLMs), as it requires fine-grained visual comprehension and multi-hop reasoning across pages. While prior work has explored reinforcement learning (RL) for enhancing advanced reasoning in MLLMs, its application to multi-page document understanding remains underexplored. In this paper, we introduce DocR1, an MLLM trained with a novel RL framework, Evidence Page-Guided GRPO (EviGRPO). EviGRPO incorporates an evidence-aware reward mechanism that promotes a coarse-to-fine reasoning strategy, guiding the model to first retrieve relevant pages before generating answers. This training paradigm enables us to build high-quality models with limited supervision. To support this, we design a two-stage annotation pipeline and a curriculum learning strategy, based on which we construct two datasets: EviBench, a high-quality training set with 4.8k examples, and ArxivFullQA, an evaluation benchmark with 8.6k QA pairs based on scientific papers. Extensive experiments across a wide range of benchmarks demonstrate that DocR1 achieves state-of-the-art performance on multi-page tasks, while consistently maintaining strong results on single-page benchmarks.

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