Elamur Research⌕
☰
home / papers / 2025 / WebDancer: Towards Autonomous Information Seeking Agency

WebDancer: Towards Autonomous Information Seeking Agency

Paper · 2025 · Jialong Wu, Baixuan Li, Runnan Fang et al.

Summary

WebDancer addresses how to train, from scratch, an open web agent that can carry out the long, multi-step research associated with systems like Deep Research. The authors argue that existing search-agent training data is too shallow (2Wiki scores already exceed 80%) and that hard benchmarks are too small to train on. They propose a four-stage recipe: synthesizing difficult QA data, collecting agent trajectories through rejection sampling, a supervised fine-tuning cold start, and on-policy RL with DAPO.

Two generators supply the data. CRAWL QA has GPT-4o write questions from subpages reached by following links out of knowledge-rich root sites. E2HQA repeatedly swaps an entity in a simple SimpleQA-style question for a searched description, which raises difficulty while keeping the answer fixed.

The resulting ReAct agent performs strongly on GAIA and WebWalkerQA. Further analysis shows that RL produces longer reasoning, and that much of the run-to-run instability comes from the changing web itself. The work gives a reproducible pathway for building deep-research agents.

Key points

  • Existing hard benchmarks are too small for training: GAIA has 466 items, WebWalkerQA 680 and BrowseComp 1,266. This motivates the synthetic CRAWL QA and E2HQA datasets, which use typed questions such as COUNT, MULTI-HOP and INTERSECTION.
  • The agent follows ReAct with a minimal action set:
    • search (query plus year filter) returns the Top-10 titles and snippets.
    • visit (goal plus URL) returns evidence summarized by a separate model.
    • answer ends the episode.
  • Trajectories come from two sources: an instruction LLM producing Short-CoT and a reasoning model (LRM) producing Long-CoT. Mixing the two styles gave no notable gain.
  • DAPO's dynamic sampling reuses QA pairs left underexploited by SFT. RL also increases both reasoning length and action count compared with the SFT model.
  • Changing the decoding temperature barely affected GAIA results. In a memorization stress test, Qwen-7B was fine-tuned on 69 correct GAIA dev trajectories for 10 epochs and still reached only 37.4% with greedy decoding on that same set, which highlights how hard it is to stabilize agents in open-web settings.

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

 Institutions

🎓Tsinghua UniversityBeijing · China333 works · 87,019 citations🎓Peking UniversityBeijing · China244 works · 39,359 citations🏢Alibaba GroupHangzhou · China168 works · 15,386 citations🎓Zhejiang UniversityHangzhou · China172 works · 12,988 citations🎓Yale UniversityNew Haven · United States95 works · 12,765 citations🎓Nanjing UniversityNanjing · China74 works · 7,814 citations🏢Alibaba DAMO AcademyHangzhou · China39 works · 6,378 citations🎓Southeast UniversityNanjing · China28 works · 354 citations

 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 worksInformation Systems · 1,437 works

Cite

Cite as Jialong Wu, Baixuan Li, Runnan Fang, Wenbiao Yin, Liwen Zhang, Zhengwei Tao, Dingchu Zhang, Zekun Xi, Gang Fu, Yong Jiang, Pengjun Xie, Fei Huang and Jingren Zhou. 2025. WebDancer: Towards Autonomous Information Seeking Agency. arXiv:2505.22648.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2505.22648 · arXiv 2505.22648

@misc{wu2025webdancer,
  title = {{WebDancer: Towards Autonomous Information Seeking Agency}},
  author = {Jialong Wu, Baixuan Li, Runnan Fang, Wenbiao Yin, Liwen Zhang, Zhengwei Tao, Dingchu Zhang, Zekun Xi, Gang Fu, Yong Jiang, Pengjun Xie, Fei Huang and Jingren Zhou},
  year = {2025},
  doi = {10.48550/arxiv.2505.22648},
  eprint = {2505.22648},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2505.22648}
}

References

70 references, 34 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. anthropic. Meet claude, 2025. URL https://www.anthropic.com/claude. ↗
  2. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  3. Shiyi Cao, Sumanth Hegde, Dacheng Li, Tyler Griggs, Shu Liu, Eric Tang, Jiayi Pan, Xingyao Wang, Akshay Malik, Graham Neubig, Kourosh Hakhamaneshi, Richard Liaw, Philipp Moritz, Matei Zaharia, Joseph E. Gonzalez, and Ion Stoica. Skyrl-v0: Train real-world long-horizon agents via reinforcement learni… find
  4. Baian Chen, Chang Shu, Ehsan Shareghi, Nigel Collier, Karthik Narasimhan, and Shunyu Yao. Fireact: Toward language agent fine-tuning. arXiv preprint arXiv:2310.05915, 2023.
  5. Mingyang Chen, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Fan Yang, Zenan Zhou, Weipeng Chen, Haofen Wang, Jeff Z Pan, et al. Learning to reason with search for llms via reinforcement learning. arXiv preprint arXiv:2503.19470, 2025. find
  6. Zehui Chen, Kuikun Liu, Qiuchen Wang, Wenwei Zhang, Jiangning Liu, Dahua Lin, Kai Chen, and Feng Zhao. Agent-flan: Designing data and methods of effective agent tuning for large language models. In Findings of the Association for Computational Linguistics ACL 2024, pp. 9354–9366, 2024.
  7. Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, et al. Synworld: Virtual scenario synthesis for agentic action knowledge refinement. arXiv preprint arXiv:2504.03561, 2025. find
  8. Lang Feng, Zhenghai Xue, Tingcong Liu, and Bo An. Group-in-group policy optimization for llm agent training, 2025. URL https://arxiv.org/abs/2505.10978. ↗
  9. Mohamed Amine Ferrag, Norbert Tihanyi, and Merouane Debbah. Reasoning beyond limits: Advances and open problems for llms. arXiv preprint arXiv:2503.22732, 2025. — [arXiv:2503.22732](https://arxiv.org/abs/2503.22732) find
  10. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Minlie Huang, Nan Duan, and Weizhu Chen. Tora: A tool-integrated reasoning agent for mathematical problem solving. ArXiv, abs/2309.17452, 2023. URL https://api.semanticscholar.org/CorpusID:263310365. ↗
  11. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025.
  12. Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps, 2020. URL https://arxiv.org/abs/2011.0 1060. ↗
  13. Mengkang Hu, Yuhang Zhou, Wendong Fan, Yuzhou Nie, Bowei Xia, Tao Sun, Ziyu Ye, Zhaoxuan Jin, Yingru Li, Zeyu Zhang, Yifeng Wang, Qianshuo Ye, Ping Luo, and Guohao Li. Owl: Optimized workforce learning for general multi-agent assistance in real-world task automation, 2025. URL https://github.com/cam… ↗
  14. Bowen Jin, Hansi Zeng, Zhenrui Yue, Dong Wang, Hamed Zamani, and Jiawei Han. Search-r1: Training llms to reason and leverage search engines with reinforcement learning. arXiv preprint arXiv:2503.09516, 2025.
  15. Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip HS Torr, Fahad Shahbaz Khan, and Salman Khan. Llm post-training: A deep dive into reasoning large language models. arXiv preprint arXiv:2502.21321, 2025. — [arXiv:2502.21321](https://arxiv.org/abs/2502.21321) find
  16. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023.
  17. Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. Camel: Communicative agents for "mind" exploration of large language model society. In Thirty-seventh Conference on Neural Information Processing Systems, 2023.
  18. Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. Search-o1: Agentic search-enhanced large reasoning models. arXiv preprint arXiv:2501.05366, 2025a.
  19. Yuetai Li, Xiang Yue, Zhangchen Xu, Fengqing Jiang, Luyao Niu, Bill Yuchen Lin, Bhaskar Ramasubramanian, and Radha Poovendran. Small models struggle to learn from strong reasoners. arXiv preprint arXiv:2502.12143, 2025b. find
  20. Xinbin Liang, Jinyu Xiang, Zhaoyang Yu, Jiayi Zhang, Sirui Hong, Sheng Fan, and Xiao Tang. Openmanus: An open-source framework for building general ai agents, 2025. URL https://doi.org/10.5281/ze nodo.15186407. — [doi:10.5281/ze](https://doi.org/10.5281/ze) find
  21. Bang Liu, Xinfeng Li, Jiayi Zhang, Jinlin Wang, Tanjin He, Sirui Hong, Hongzhang Liu, Shaokun Zhang, Kaitao Song, Kunlun Zhu, et al. Advances and challenges in foundation agents: From brain-inspired intelligence to evolutionary, collaborative, and safe systems. arXiv preprint arXiv:2504.01990, 2025a… — [arXiv:2504.01990](https://arxiv.org/abs/2504.01990) find
  22. Zijun Liu, Peiyi Wang, Runxin Xu, Shirong Ma, Chong Ruan, Peng Li, Yang Liu, and Yu Wu. Inference-time scaling for generalist reward modeling. arXiv preprint arXiv:2504.02495, 2025b.
  23. Xinji Mai, Haotian Xu, Xing W, Weinong Wang, Yingying Zhang, and Wenqiang Zhang. Agent rl scaling law: Agent rl with spontaneous code execution for mathematical problem solving, 2025. find
  24. Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. When not to trust language models: Investigating effectiveness of parametric and non-parametric memories, 2022. find
  25. Grégoire Mialon, Clémentine Fourrier, Thomas Wolf, Yann LeCun, and Thomas Scialom. Gaia: a benchmark for general ai assistants. In The Twelfth International Conference on Learning Representations, 2023.
  26. OpenAI. Gpt-4 system card, 2022. URL https://cdn.openai.com/papers/gpt-4-system-card.pdf. ↗
  27. OpenAI. Deep research system card, 2025a. URL https://cdn.openai.com/deep-research-system-c ard.pdf. ↗
  28. OpenAI. Introducing simpleqa, 2025b. URL https://openai.com/index/introducing-simpleqa/. ↗
  29. Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, John Ling, Sean Shi, et al. Humanity’s last exam. arXiv preprint arXiv:2501.14249, 2025.
  30. Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis. Measuring and narrowing the compositionality gap in language models, 2022. find
  31. Cheng Qian, Emre Can Acikgoz, Qi He, Hongru Wang, Xiusi Chen, Dilek Hakkani-Tür, Gokhan Tur, and Heng Ji. Toolrl: Reward is all tool learning needs. arXiv preprint arXiv:2504.13958, 2025. find
  32. Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Jiang, Chengfei Lv, and Huajun Chen. Autoact: Automatic agent learning from scratch for qa via self-planning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers… find
  33. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36:53728–53741, 2023.
  34. ByteDance Seed, Yufeng Yuan, Yu Yue, Mingxuan Wang, Xiaochen Zuo, Jiaze Chen, Lin Yan, Wenyuan Xu, Chi Zhang, Xin Liu, et al. Seed-thinking-v1. 5: Advancing superb reasoning models with reinforcement learning. arXiv preprint arXiv:2504.13914, 2025.
  35. Priyanka Sen, Alham Fikri Aji, and Amir Saffari. Mintaka: A complex, natural, and multilingual dataset for end-to-end question answering. In Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kuroha… find
  36. Yijia Shao, Yucheng Jiang, Theodore A Kanell, Peter Xu, Omar Khattab, and Monica S Lam. Assisting in writing wikipedia-like articles from scratch with large language models. In NAACL-HLT, 2024.
  37. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. Hybridflow: A flexible and efficient rlhf framework. arXiv preprint arXiv: 2409.19256, 2024.
  38. Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen. R1-searcher: Incentivizing the search capability in llms via reinforcement learning. arXiv preprint arXiv:2503.05592, 2025.
  39. Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Lei Fang, Zhongyuan Wang, Wayne Xin Zhao, and Ji-Rong Wen. Simpledeepsearcher: Deep information seeking via web-powered reasoning trajectory synthesis. 2025. URL https://github.com/RUCAIBox/SimpleDeepSear cher. ↗
  40. Gokul Swamy, Sanjiban Choudhury, Wen Sun, Zhiwei Steven Wu, and J. Andrew Bagnell. All roads lead to likelihood: The value of reinforcement learning in fine-tuning, 2025. URL https://arxiv.org/abs/ 2503.01067. ↗
  41. QwQ Team. Qwq-32b: Embracing the power of reinforcement learning, 2025a. URL https://qwenlm.g ithub.io/blog/qwq-32b/. ↗
  42. WebThinker Team. Webthinker: Empowering large reasoning models with deep research capability, 2025b. URL https://github.com/RUC-NLPIR/WebThinker. Github. ↗
  43. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Musique: Multihop questions via single-hop question composition, 2022. URL https://arxiv.org/abs/2108.00573. ↗
  44. Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, Alex Tachard Passos, William Fedus, and Ame lia Glaese. Browsecomp: A simple yet challenging benchmark for browsing agents. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc… — Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  45. Yuxiang Wei, Olivier Duchenne, Jade Copet, Quentin Carbonneaux, Lingming Zhang, Daniel Fried, Gabriel Synnaeve, Rishabh Singh, and Sida I. Wang. Swe-rl: Advancing llm reasoning via reinforcement learning on open software evolution, 2025a. find
  46. Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, and Lihong Li. Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning, 2025b. URL https://arxiv.org/abs/2505.16421. ↗
  47. Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Deyu Zhou, Pengjun Xie, and Fei Huang. Webwalker: Benchmarking llms in web traversal, 2025. URL https://arxiv.org/ab s/2501.07572. ↗
  48. Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng, Minghao Liu, Jialong Wu, Jiachen Ma, Yizhi Li, Jian Yang, Wangchunshu Zhou, Qunshu Lin, Junbo Zhao, Zhaoxiang Zhang, Wenhao Huang, Ge Zhang, Chenghua Lin, and J. H. Liu. A comparative study on reasoning patterns of openai’s o1 model, 2024. URL https:… — [arXiv:2410.13639](https://arxiv.org/abs/2410.13639) find
  49. Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Ningyu Zhang, Jiang Yong, Pengjun Xie, Fei Huang, and Huajun Chen. Omnithink: Expanding knowledge boundaries in machine writing through thinking. arXiv preprint arXiv:2501.09751, 2025.
  50. Wenyuan Xu, Xiaochen Zuo, Chao Xin, Yu Yue, Lin Yan, and Yonghui Wu. A unified pairwise framework for rlhf: Bridging generative reward modeling and policy optimization. arXiv preprint arXiv:2504.04950, 2025. — [arXiv:2504.04950](https://arxiv.org/abs/2504.04950) find
  51. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115, 2024. find
  52. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jing Zhou, Jin… — Qwen3 Technical Report
  53. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering, 2018. URL https://arxiv.org/abs/1809.09600. ↗
  54. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR), 2023.
  55. Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni, Bo Zeng, Hao Wang, Tianqi Shi, Liangying Shao, Chenyang Lyu, Longyue Wang, Weihua Luo, and Kaifu Zhang. Towards widening the distillation bottleneck for reasoning models, 2025. find
  56. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, et al. Dapo: An open-source llm reinforcement learning system at scale. arXiv preprint arXiv:2503.14476, 2025.
  57. Yuanqing Yu, Zhefan Wang, Weizhi Ma, Zhicheng Guo, Jingtao Zhan, Shuai Wang, Chuhan Wu, Zhiqiang Guo, and Min Zhang. Steptool: A step-grained reinforcement learning framework for tool learning in llms. arXiv preprint arXiv:2410.07745, 2024.
  58. Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Yang Yue, Shiji Song, and Gao Huang. Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?, 2025. URL https://arxiv.org/abs/2504.13837. ↗
  59. Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang, Xiao Liu, Yuxiao Dong, and Jie Tang. Agenttuning: Enabling generalized agent abilities for llms. In Findings of the Association for Computational Linguistics ACL 2024, pp. 3053–3077, 2024.
  60. Chaoyun Zhang, Shilin He, Liqun Li, Si Qin, Yu Kang, Qingwei Lin, and Dongmei Zhang. Api agents vs. gui agents: Divergence and convergence. arXiv preprint arXiv:2503.11069, 2025a. — [arXiv:2503.11069](https://arxiv.org/abs/2503.11069) find
  61. Chong Zhang, Yue Deng, Xiang Lin, Bin Wang, Dianwen Ng, Hai Ye, Xingxuan Li, Yao Xiao, Zhanfeng Mo, Qi Zhang, et al. 100 days after deepseek-r1: A survey on replication studies and more directions for reasoning language models. arXiv preprint arXiv:2505.00551, 2025b.
  62. Dingchu Zhang, Yida Zhao, Jialong Wu, Baixuan Li, Wenbiao Yin, Liwen Zhang, Yong Jiang, Yufeng Li, Kewei Tu, Pengjun Xie, and Fei Huang. Evolvesearch: An iterative self-evolving search agent, 2025c. URL https://arxiv.org/abs/2505.22501. ↗
  63. Shaokun Zhang, Yi Dong, Jieyu Zhang, Jan Kautz, Bryan Catanzaro, Andrew Tao, Qingyun Wu, Zhiding Yu, and Guilin Liu. Nemotron-research-tool-n1: Tool-using language models with reinforced reasoning. arXiv preprint arXiv:2505.00024, 2025d. — [arXiv:2505.00024](https://arxiv.org/abs/2505.00024) find
  64. Yuxiang Zhang, Yuqi Yang, Jiangming Shu, Xinyan Wen, and Jitao Sang. Agent models: Internalizing chain-of-action generation into reasoning models. arXiv preprint arXiv:2503.06580, 2025e. — [arXiv:2503.06580](https://arxiv.org/abs/2503.06580) find
  65. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems, 36:46595–46623, 2023.
  66. Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, and Pengfei Liu. Deepresearcher: Scaling deep research via reinforcement learning in real-world environments, 2025. URL https://arxiv.org/abs/2504.03160. ↗
  67. Peilin Zhou, Bruce Leon, Xiang Ying, Can Zhang, Yifan Shao, Qichen Ye, Dading Chong, Zhiling Jin, Chenxuan Xie, Meng Cao, et al. Browsecomp-zh: Benchmarking web browsing ability of large language models in chinese. arXiv preprint arXiv:2504.19314, 2025. — [arXiv:2504.19314](https://arxiv.org/abs/2504.19314) find
  68. Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li, Jialong Wu, Tiannan Wang, Shi Qiu, Jintian Zhang, Jing Chen, Ruipu Wu, Shuai Wang, Shiding Zhu, Jiyu Chen, Wentao Zhang, Xiangru Tang, Ningyu Zhang, Huajun Chen, Peng Cui, and Mrinmaya Sachan. Agents: An open-source framework for autonomous language a… find
  69. Wangchunshu Zhou, Yixin Ou, Shengwei Ding, Long Li, Jialong Wu, Tiannan Wang, Jiamin Chen, Shuai Wang, Xiaohua Xu, Ningyu Zhang, Huajun Chen, and Yuchen Eleanor Jiang. Symbolic learning enables self-evolving agents. 2024. URL https://arxiv.org/abs/2406.18532. ↗
  70. Yuxin Zuo, Kaiyan Zhang, Shang Qu, Li Sheng, Xuekai Zhu, Biqing Qi, Youbang Sun, Ganqu Cui, Ning Ding, and Bowen Zhou. Ttrl: Test-time reinforcement learning, 2025. URL https://arxiv.org/abs/ 2504.16084. ↗

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/wu-2025-webdancer-towards-autonomous-information-seeking/