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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query_id
dtype: int64
- name: query
dtype: string
- name: answer
dtype: string
- name: gold_docs
list:
- name: position
dtype: int64
- name: text
dtype: string
- name: url
dtype: string
- name: pass_rate
dtype: float64
splits:
- name: train
num_examples: 6102
license: mit
---
<div style="display: flex; align-items: center; justify-content: center; gap: 8px;">
<img src="imgs/or-logo1.png" style="height: 84px; width: auto;">
<img src="imgs/openresearcher-title.svg" style="height: 84px; width: auto;">
</div>
<div align="center">
<a href="https://arxiv.org/abs/2603.20278"><img src="https://img.shields.io/badge/arXiv-B31B1B?style=for-the-badge&logo=arXiv&logoColor=white" alt="Blog"></a>
<a href="https://huggingface.co/papers/2603.20278"><img src="https://img.shields.io/badge/Paper-FFD966?style=for-the-badge&logo=huggingface&logoColor=ffffff" alt="Model"></a>
<a href="https://github.com/TIGER-AI-Lab/OpenResearcher"><img src="https://img.shields.io/badge/Github-181717?style=for-the-badge&logo=github&logoColor=white" alt="Blog"></a>
<a href="https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Dataset"><img src="https://img.shields.io/badge/Dataset-FFB7B2?style=for-the-badge&logo=huggingface&logoColor=ffffff" alt="Dataset"></a>
<a href="https://huggingface.co/OpenResearcher/Nemotron-3-Nano-30B-A3B"><img src="https://img.shields.io/badge/Model-FFD966?style=for-the-badge&logo=huggingface&logoColor=ffffff" alt="Model"></a>
<a href="https://huggingface.co/spaces/OpenResearcher/OpenResearcher"><img src="https://img.shields.io/badge/Demo-F97316.svg?style=for-the-badge&logo=gradio&logoColor=white" alt="Demo"></a>
<a href="https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Eval-Logs/tree/main"><img src="https://img.shields.io/badge/Eval%20Logs-755BB4?style=for-the-badge&logo=google-sheets&logoColor=white" alt="Eval Logs"></a>
</div>
## OpenResearcher Gold Documents
This dataset contains the **gold documents** used for the "Gold Document Retrieval via Online Bootstrapping" step described in Section 3.2 of the [OpenResearcher paper](https://arxiv.org/abs/2603.20278). Gold documents are documents that collectively contain sufficient evidence to derive the ground-truth answer for a given question.
For **6,102** questions sourced from [MiroVerse](https://huggingface.co/datasets/miromind-ai/MiroVerse-v0.1), we constructed a search query by concatenating the question and reference answer, retrieved web content via the Serper API, and cleaned/deduplicated the results to obtain **~10K gold documents** (1–3 gold documents per question, averaging 2.86).
These gold documents are merged with 15M FineWeb documents (as distractors) to build the offline search corpus, [OpenResearcher-Corpus](https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Corpus), which is used as a self-hosted, API-free search engine when synthesizing the deep-research trajectories released as [OpenResearcher-Dataset](https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Dataset). This bootstrapping step is essential: removing it causes gold-document hit rate to drop from 29.54% to 1.73%, trajectory accuracy to drop from 56.86% to 43.81%, and downstream BrowseComp-Plus accuracy to collapse from 54.81% to 6.35% (see RQ2 in the paper).
## Format
Each row in the dataset contains the following fields:
- **query_id** (int64): A unique identifier for each question.
- **query** (string): The original question, sourced from [MiroVerse](https://huggingface.co/datasets/miromind-ai/MiroVerse-v0.1).
- **answer** (string): The reference answer used to construct the retrieval query.
- **gold_docs** (list): The gold documents retrieved and cleaned for this question. Each entry contains:
- **position** (int64): Rank position among the retrieved gold documents.
- **text** (string): The full text content of the gold document.
- **url** (string): The source URL where the document was retrieved from.
- **pass_rate** (float64): The pass rate observed for this question during trajectory synthesis with GPT-OSS-120B.
## How to use this dataset?
```python
from datasets import load_dataset
ds = load_dataset("OpenResearcher/OpenResearcher-Corpus-Gold-Doc", split="train")
row = ds[0]
print(row["query"])
print(row["answer"])
for doc in row["gold_docs"]:
print(doc["url"], doc["text"][:200])
```
## Related Resources
- Paper: [OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis](https://arxiv.org/abs/2603.20278)
- Offline search corpus (gold docs + FineWeb distractors, embedded and indexed): [OpenResearcher/OpenResearcher-Corpus](https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Corpus)
- Synthesized training trajectories: [OpenResearcher/OpenResearcher-Dataset](https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Dataset)
- Code: [TIGER-AI-Lab/OpenResearcher](https://github.com/TIGER-AI-Lab/OpenResearcher)
## Core Contributors
<table>
<tr>
<td align="center">
<a href="https://zhuofeng-li.github.io/">
<img src="https://github.com/Zhuofeng-Li.png" width="75px;" alt="Zhuofeng Li"/>
<br />
<sub><b>Zhuofeng Li</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/jdf-prog">
<img src="https://github.com/jdf-prog.png" width="75px;" alt="Dongfu Jiang"/>
<br />
<sub><b>Dongfu Jiang</b></sub>
</a>
</td>
</td>
<td align="center">
<a href="https://mxueguang.github.io/">
<img src="https://mxueguang.github.io/images/profile.jpg" width="75px;" alt="Xueguang"/>
<br />
<sub><b>Xueguang Ma</b></sub>
</a>
</td>
<td align="center">
<a href="https://isaacghx.github.io/about/">
<img src="https://github.com/IsaacGHX.png" width="75px;" alt="Haoxiang Zhang"/>
<br />
<sub><b>Haoxiang Zhang</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/erenup">
<img src="https://github.com/erenup.png" width="75px;" alt="Ping Nie"/>
<br />
<sub><b>Ping Nie</b></sub>
</a>
</td>
</tr>
</table>
## Advisors
<table>
<tr>
<td align="center">
<a href="https://github.com/wenhuchen">
<img src="https://github.com/wenhuchen.png" width="75px;" alt="Wenhu Chen"/>
<br />
<sub><b>Wenhu Chen</b></sub>
</a>
</td>
<td align="center">
<a href="https://yuzhimanhua.github.io/">
<img src="https://yuzhimanhua.github.io/profile_pic.jpg" width="75px;" alt="Yu Zhang"/>
<br />
<sub><b>Yu Zhang</b></sub>
</a>
</td>
</tr>
</table>
## Acknowledgements
<div align="center">
<img src="https://raw.githubusercontent.com/TIGER-AI-Lab/OpenResearcher/main/assets/imgs/ack.png" alt="Deep Research Benchmark Results" width="100%">
</div>
## Citation
```bibtex
@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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