@inproceedings{lin-etal-2026-mm,
    title = "{MM}-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning",
    author = "Lin, Jiahang  and
      Hu, Kai  and
      Wang, Binghai  and
      Zhou, Yuhao  and
      Xi, Zhiheng  and
      Guo, Honglin  and
      Liu, Shichun  and
      Wang, Junzhe  and
      Dou, Shihan  and
      Zhou, Enyu  and
      Yan, Hang  and
      Han, Zhenhua  and
      Gui, Tao  and
      Zhang, Qi  and
      Huang, Xuanjing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1488/",
    pages = "29770--29783",
    ISBN = "979-8-89176-395-1",
    abstract = "Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass retrieval. We introduce **MM-Doc-R1**, a novel framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis. To incentivize the information seeking capabilities of our agents, we propose **Similarity-based Policy Optimization (SPO)**, addressing baseline estimation bias in existing multi-turn reinforcement learning (RL) algorithms like GRPO. Our core insight is that in multi-turn RL, the more semantically similar two trajectories are, the more accurate their shared baseline estimation becomes. Leveraging this, SPO calculates a more precise baseline by similarity-weighted averaging of rewards across multiple trajectories, unlike GRPO which inappropriately applies the initial state{'}s baseline to all intermediate states. This provides a more stable and accurate learning signal for our agents, leading to superior training performance that surpasses GRPO. Our experiments on the MMLongbench-Doc benchmark show that **MM-Doc-R1** outperforms previous baselines by **10.4{\%}**. Furthermore, **SPO** demonstrates superior performance over **GRPO**, boosting results by **5.0{\%}** with Qwen3-8B and **6.1{\%}** with Qwen3-4B. These results highlight the effectiveness of our integrated framework and novel training algorithm in advancing the state-of-the-art for complex, long-document visual question answering."
}