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Visually-Guided Policy Optimization for Multimodal Reasoning

Paper · 2026 · Zengbin Wang, Feng Xiong, Liang Lin et al.

Summary

Vision-language models trained with reinforcement learning with verifiable rewards (RLVR) reason better step by step. However, they tend to rely on text and pay little attention to the image. The authors show that attention to visual tokens is sparse, and it fades as the reasoning chain grows longer, a pattern they call temporal visual forgetting. Wrong answers show this decay more often than correct ones.

To address this, they propose Visually-Guided Policy Optimization (VGPO), built on GRPO with DAPO-style settings. VGPO adds no extra tokens, auxiliary models or extra forward passes. Instead, it uses the similarity between hidden states of generated tokens and image tokens as an internal Visual Focus Score. A Visual Attention Compensation mechanism uses this score to boost visually grounded tokens and to raise the expected visual focus in later reasoning steps. The compensation enters training through advantage re-weighting at two levels: tokens within a trajectory, and whole trajectories compared with each other.

According to the authors, VGPO keeps attention on the image throughout generation and reaches state-of-the-art results on mathematical and vision-dependent multimodal reasoning.

Key points

  • The diagnostic analysis uses Qwen2.5-VL-7B on four visual-dependent benchmarks: LogicVista, CLEVR Counting, MMMU-Pro and MathVerse-V.
  • Correct samples keep more of their visual attention late in reasoning than incorrect ones. The average late/early visual accumulation ratio is 0.680 for correct samples versus 0.532 for incorrect ones.
  • VGPO differs from Look-Back, latent-token methods, VAPO (which uses a GPT-5 verifier), PAPO and VPPO (which compare clean and noisy images via KL). It relies only on the model's own hidden states.
  • Applying compensation only in later reasoning steps worked better than applying it early, since early steps already attend to the image.
  • Stated limitations:
    • Raising visual expectations over time is a heuristic.
    • Gains are capped by the quality of the vision encoder and projector.
  • Code: github.com/wzb-bupt/VGPO.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1,426 worksVision & language · 657 works

Cite

Cite as Zengbin Wang, Feng Xiong, Liang Lin, Xuecai Hu, Yong Wang, Yanlin Wang, Man Zhang and Xiangxiang Chu. 2026. Visually-Guided Policy Optimization for Multimodal Reasoning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.301

@inproceedings{wang2026visually,
  title = {{Visually-Guided Policy Optimization for Multimodal Reasoning}},
  author = {Zengbin Wang, Feng Xiong, Liang Lin, Xuecai Hu, Yong Wang, Yanlin Wang, Man Zhang and Xiangxiang Chu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.301},
  url = {https://doi.org/10.18653/v1/2026.acl-long.301}
}

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