| --- |
| 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) |
| |
| ## 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} |
| } |
| ``` |
| |
