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
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. 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, 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, which is used as a self-hosted, API-free search engine when synthesizing the deep-research trajectories released as 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.
- 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?
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
- Offline search corpus (gold docs + FineWeb distractors, embedded and indexed): OpenResearcher/OpenResearcher-Corpus
- Synthesized training trajectories: OpenResearcher/OpenResearcher-Dataset
- Code: TIGER-AI-Lab/OpenResearcher
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}
}