@inproceedings{hong-etal-2026-view,
    title = "View-R1: Asymmetric Policy Optimization for Difficulty-Aware Multimodal Reinforcement Learning",
    author = "Hong, Minjie  and
      Guo, Zirun  and
      Zhang, Jiabao  and
      Wang, Zehan  and
      Zhang, Ziang  and
      Jin, Tao  and
      Zhao, Zhou",
    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.1538/",
    pages = "30791--30803",
    ISBN = "979-8-89176-395-1",
    abstract = "Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data but often struggle with complex reasoning. Reinforcement learning (RL) can enhance reasoning, yet it may cause performance degradation on general tasks and overthinking in MLLMs. We propose Asymmetric Policy Optimization (APO), which separates responses into positive and negative groups. For positive samples, Difficulty-Adaptive Divergence Shaping (DADS) dynamically adjusts the KL weight to stabilize training and preserve knowledge. For negative samples, Suboptimal Trajectory Complexity Regularization (STCR) penalizes overly long responses to reduce overthinking. Applied to Qwen2.5-VL, our model View-R1 achieves a 10.55{\%} improvement in reasoning and outperforms larger models (7{--}11B) while not only maintaining but also slightly improving performance on general tasks. These results highlight the effectiveness and broad applicability of our DADS and STCR techniques for advancing complex multimodal reasoning in MLLMs. Our code is available at https://github.com/Collab-Gen/View-R1."
}