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README.md

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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)
+
+## 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}
+}
+```