---
title: "DORA — paper by Qin et al. (2026) — summary and ideas"
description: "Multimodal LLMs trained with reinforcement learning can call search engines while answering knowledge-heavy visual questions. 33 references."
authors: ["Guangming Qin", "Yuhao Deng", "Yukun Zhao", "Zhenyang Li", "Junfeng Wang", "Dawei Yin", "Ye Yuan", "Guoren Wang", "Yizhou Yan", "Chengliang Chai", "Lei Cao"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.605/"
doi: "10.18653/v1/2026.acl-long.605"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/qin-2026-dora-dual-objective-reinforcement-learning/"
---
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# DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search

**Authors:** Guangming Qin, Yuhao Deng, Yukun Zhao, [Zhenyang Li](https://md.research.elamur.ai/free/en/authors/zhenyang-li/), Junfeng Wang, [Dawei Yin](https://md.research.elamur.ai/free/en/authors/dawei-yin/), [Ye Yuan](https://md.research.elamur.ai/free/en/authors/ye-yuan/), Guoren Wang, Yizhou Yan, Chengliang Chai and Lei Cao · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.605/) · [PDF](https://aclanthology.org/2026.acl-long.605.pdf) · [DOI 10.18653/v1/2026.acl-long.605](https://doi.org/10.18653/v1/2026.acl-long.605) · [OpenAlex](https://openalex.org/W7166813736)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Multimodal LLMs trained with reinforcement learning can call search engines while answering knowledge-heavy visual questions. However, current training rewards only the final answer. A trajectory with good retrieval but a wrong answer is penalized as a whole, and a correct path with redundant searches earns the same credit as a lean one.

DORA tackles both problems on top of GRPO. Each rollout follows a fixed cycle: thinking, issuing text or image queries to a multimodal retriever (e.g., LamRA, UniIR), reflecting on whether the evidence is useless, partial or sufficient, concluding, and answering. Two training ideas drive the method:
- **Efficiency reward:** a group-wise score that ranks correct sampled paths by how few search calls they needed.
- **Dual objective:** a retrieval-quality objective that touches only the search steps, while the answer-correctness objective covers the whole trajectory.

On Enc-VQA, InfoSeek and SK-VQA, DORA outperforms baselines such as MMSearch-R1. It improves accuracy by up to 8.4% and issues roughly 9.7% fewer searches. This points toward cheaper and more reliable retrieval agents for knowledge-intensive VQA (KI-VQA).

## Key points
- **Evaluation:** three benchmarks (Enc-VQA, InfoSeek, SK-VQA). The paper is internally inconsistent on the best gain: the abstract reports up to 8.4% higher accuracy, while the contributions list states 7.8%. Both cite a 9.7% cut in search calls.
- **Full DORA results:** accuracy of 25.01 / 44.53 / 40.98 on the three datasets, with an average of 1.64 / 1.18 / 1.08 search calls per query.
- **Where the efficiency comes from:** compared with MMSearch-R1 on Enc-VQA, irrelevant searches drop from 0.51 to 0.36 per query, while effective searches rise slightly from 1.23 to 1.28.
- **Ablations:**
  - Removing the efficiency reward barely changes accuracy but increases search calls by about 59%.
  - Removing the dual objective lowers accuracy by about 7.5%.
  - Removing the reflection step costs 3–4% accuracy.
- **Limitations:** the reward scores need careful tuning, and the framework currently covers only text-image reasoning. Video and multi-agent settings are left for future work.

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

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- [Baidu](https://md.research.elamur.ai/free/en/companies/baidu/) — Company, Beijing, China · 74 works · 15,263 citations
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- [Beijing Institute of Technology](https://md.research.elamur.ai/free/en/universities/beijing-institute-of-technology/) — University, Beijing, China · 34 works · 3,652 citations
- [Shandong University](https://md.research.elamur.ai/free/en/universities/shandong-university/) — University, Jinan, China · 17 works · 789 citations

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## Cite

Cite as: Guangming Qin, Yuhao Deng, Yukun Zhao, Zhenyang Li, Junfeng Wang, Dawei Yin, Ye Yuan, Guoren Wang, Yizhou Yan, Chengliang Chai and Lei Cao. 2026. DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search. 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.

```bibtex
@inproceedings{qin2026dora,
  title = {{DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search}},
  author = {Guangming Qin, Yuhao Deng, Yukun Zhao, Zhenyang Li, Junfeng Wang, Dawei Yin, Ye Yuan, Guoren Wang, Yizhou Yan, Chengliang Chai and Lei Cao},
  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.605},
  url = {https://doi.org/10.18653/v1/2026.acl-long.605}
}
```

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32. Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, and Xing Yu. 2025. Deepeyes: Incentivizing "thinking with images" via reinforcement learning. Preprint, arXiv:2505.14362. sufficient knowledge has been gathered to produce a final answer. — [arXiv:2505.14362](https://arxiv.org/abs/2505.14362) [find](https://scholar.google.com/scholar?q=Ziwei+Zheng%2C+Michael+Yang%2C+Jack+Hong%2C+Chenxiao+Zhao%2C+Guohai+Xu%2C+Le+Yang%2C+Chao+Shen%2C+and+Xing+Yu%2E+2025%2E+Deepeyes%3A+Incentivizing+%22thinking+with+images%22+via+reinforcement+learning%2E+Preprint%2C+arXiv%3A2505%2E1)
33. Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, Zhangwei Gao, Erfei Cui, Xuehui Wang, Yue Cao, Yangzhou Liu, Xingguang Wei, Hongjie Zhang, Haomin Wang, Weiye Xu, and 32 others. 2025. [Internvl3: Exploring advanced training and test-…](https://md.research.elamur.ai/free/en/papers/zhu-2025-internvl3-exploring-advanced-training/)

*Updated 1 November 2026*