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.
@article{arxiv.2604.13579,
title = {MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning},
author = {Jiahang Lin and Kai Hu and Binghai Wang and Yuhao Zhou and Zhiheng Xi and Honglin Guo and Shichun Liu and Junzhe Wang and Shihan Dou and Enyu Zhou and Hang Yan and Zhenhua Han and Tao Gui and Qi Zhang and Xuanjing Huang},
journal= {arXiv preprint arXiv:2604.13579},
year = {2026}
}