@inproceedings{li-etal-2026-look,
    title = "Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning",
    author = "Li, Xuchen  and
      Li, Xuzhao  and
      Gao, Jiahui  and
      Pi, Renjie  and
      Hu, Shiyu  and
      Zhang, Wentao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.225/",
    pages = "4948--4967",
    ISBN = "979-8-89176-390-6",
    abstract = "Vision-Language Models (VLMs) excel at many multimodal tasks, yet they frequently struggle with tasks requiring precise understanding and handling of fine-grained visual elements. This is mainly due to information loss during image encoding or insufficient attention to critical regions. Recent work has shown promise by incorporating pixel-level visual information into the reasoning process, enabling VLMs to access high-resolution visual details during their thought process. However, this pixel-level information is often overused, leading to inefficiency and distraction from irrelevant visual details. To address these challenges, we propose the first framework for adaptive pixel reasoning that dynamically determines necessary pixel-level operations based on the input query. Specifically, we first apply operation-aware supervised fine-tuning to establish baseline competence in textual reasoning and visual operations, then design a novel rollout-guided reinforcement learning framework relying on feedback of the model{'}s own responses, which enables the VLM to determine when pixel operations should be invoked based on query difficulty. Experiments on extensive multimodal reasoning benchmarks show that our model achieves superior performance while significantly reducing unnecessary visual operations. Impressively, our model achieves 73.4{\%} accuracy on HR-Bench 4K while maintaining a tool usage ratio of only 20.1{\%}, improving accuracy and simultaneously reducing tool usage by 66.5{\%} compared to the previous methods."
}