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Adopted by NVIDIA's Nemotron family of models!

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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
Zhuofeng Li
Dongfu Jiang
Dongfu Jiang
Xueguang
Xueguang Ma
Haoxiang Zhang
Haoxiang Zhang
Ping Nie
Ping Nie

Advisors

Wenhu Chen
Wenhu Chen
Yu Zhang
Yu Zhang

Acknowledgements

Deep Research Benchmark Results

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}
}
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7.46 GB
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Contributors