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| .gitattributes | 2.46 kB xet | 19463de8 | |
| README.md | 61.4 kB xet | 6d8feb0e |
🤗 HuggingFace |
Slack | WeChat
Overview
OpenResearcher is a fully open agentic large language model (30B-A3B) designed for long-horizon deep research scenarios. It achieves an impressive 54.8% accuracy on BrowseComp-Plus, surpassing performance of GPT-4.1, Claude-Opus-4, Gemini-2.5-Pro, DeepSeek-R1 and Tongyi-DeepResearch. It also demonstrates leading performance across a range of deep research benchmarks, including BrowseComp, GAIA, WebWalkerQA, and xbench-DeepSearch. We fully open-source the training and evaluation recipe—including data, model, training methodology, and evaluation framework for everyone to progress deep research.
OpenResearcher Training Dataset
Our training dataset consists of 96K high-quality long-horizon DeepResearch trajectories with 100+ turns generated by GPT-OSS-120B using its native browser tools. To enable scalable and cost-efficient data generation, we deploy a self-hosted search engine over carefully constructed ~11B-token corpus , completely eliminating reliance on external search APIs.
Format
Each row in the dataset contains the following fields:
qid (int64): A unique identifier for each question or task.
question (string): The original deepresearch question compiled from MiroVerse.
answer (string): The final answer to the question.
messages (list): A list of messages representing the GPT-OSS 120B deep research trajectory, including intermediate reasoning steps, tool calls, observations, and model responses throughout the problem-solving process.
Core Contributors
Zhuofeng Li |
Dongfu Jiang |
Xueguang Ma |
Haoxiang Zhang |
Ping Nie |
Advisors
Wenhu Chen |
Yu Zhang |
Acknowledgements
Citation
@article{li2026openresearcher,
title={{OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis}},
author={Li, Zhuofeng and Jiang, Dongfu and Ma, Xueguang and Zhang, Haoxiang and Nie, Ping and Zhang, Yuyu and Zou, Kai and Xie, Jianwen and Zhang, Yu and Chen, Wenhu},
journal={arXiv preprint arXiv:2603.20278},
year={2026}
}
- Total size
- 7.46 GB
- Files
- 60
- Last updated
- Nov 12
- Pre-warmed CDN
- US EU US EU