This is the training dataset for DeepDive, an automated approach for training deep search agents with complex, multi-step reasoning capabilities. The dataset is constructed through automated knowledge graph random walks, entity obfuscation, and difficulty filtering to create challenging questions that require sophisticated search and retrieval skills.
| Component | Split | Size | Description |
|---|---|---|---|
| Total Dataset | qa_sft, qa_rl | 3,250 | Complete collection of QA pairs |
| SFT Portion | qa_sft | 1,016 | Question-answer pairs for Supervised Fine-Tuning |
| ↳ SFT Trajectories | trajectories_sft | 858 | Search trajectories from SFT QA pairs via reject sampling |
| RL Portion | qa_rl | 2,234 | Question-answer pairs for Reinforcement Learning |
qa_sft and qa_rl SplitFields:
id: Unique identifier for the QA pairquestion: Multi-hop reasoning question requiring searchanswer: Ground truth answerconversation: [] (empty)trajectories_sft SplitFields:
id: Unique identifier for the trajectoryquestion: The original questionanswer: Ground truth answerconversation: List of conversation turns showing the search process (role, content)from datasets import load_dataset
dataset = load_dataset("zai-org/DeepDive")
# Access splits
sft_data = dataset["qa_sft"]
rl_data = dataset["qa_rl"]
trajectories = dataset["trajectories_sft"]
This dataset is released under the Apache License 2.0.
If you find DeepDive useful for your research, please cite our paper:
@misc{lu2025deepdiveadvancingdeepsearch,
title={DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL},
author={Rui Lu and Zhenyu Hou and Zihan Wang and Hanchen Zhang and Xiao Liu and Yujiang Li and Shi Feng and Jie Tang and Yuxiao Dong},
year={2025},
eprint={2509.10446},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.10446},
}
This is the training dataset for DeepDive, an automated approach for training deep search agents with complex, multi-step reasoning capabilities. The dataset is constructed through automated knowledge graph random walks, entity obfuscation, and difficulty filtering to create challenging questions that require sophisticated search and retrieval skills.
| Component | Split | Size | Description |
|---|---|---|---|
| Total Dataset | qa_sft, qa_rl | 3,250 | Complete collection of QA pairs |
| SFT Portion | qa_sft | 1,016 | Question-answer pairs for Supervised Fine-Tuning |
| ↳ SFT Trajectories | trajectories_sft | 858 | Search trajectories from SFT QA pairs via reject sampling |
| RL Portion | qa_rl | 2,234 | Question-answer pairs for Reinforcement Learning |
qa_sft and qa_rl SplitFields:
id: Unique identifier for the QA pairquestion: Multi-hop reasoning question requiring searchanswer: Ground truth answerconversation: [] (empty)trajectories_sft SplitFields:
id: Unique identifier for the trajectoryquestion: The original questionanswer: Ground truth answerconversation: List of conversation turns showing the search process (role, content)from datasets import load_dataset
dataset = load_dataset("zai-org/DeepDive")
# Access splits
sft_data = dataset["qa_sft"]
rl_data = dataset["qa_rl"]
trajectories = dataset["trajectories_sft"]
This dataset is released under the Apache License 2.0.
If you find DeepDive useful for your research, please cite our paper:
@misc{lu2025deepdiveadvancingdeepsearch,
title={DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL},
author={Rui Lu and Zhenyu Hou and Zihan Wang and Hanchen Zhang and Xiao Liu and Yujiang Li and Shi Feng and Jie Tang and Yuxiao Dong},
year={2025},
eprint={2509.10446},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.10446},
}