GAIR/DeepResearcher-7b

Introduction

DeepResearcher is the first comprehensive framework for end-to-end training of LLM-based deep research agents through scaling reinforcement learning (RL) in real-world environments with authentic web search interactions. Our qualitative analysis reveals emergent cognitive behaviors from end-to-end RL training, including the ability to formulate plans, cross-validate information from multiple sources, engage in self-reflection to redirect research, and maintain honesty when unable to find definitive answers.

Model Details

• License: Apache 2.0
• Model type: Reinforcement learning-based LLM (Large Language Model).
• Language(s): The model is designed for tasks in English.
• Finetuned from model: The model is built using the Qwen2.5-7B-Instruct architecture .

Model Description

Model Sources

• Repository: DeepResearcher GitHub .
• Paper: DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

How to Get Started with the Model

To get started, you can visit the DeepResearcher repository on GitHub, where the model's code and setup instructions are provided .

Training Details

Training Data

The model was trained on open-domain question-answering datasets, including:

• NaturalQuestions (NQ)
• TriviaQA (TQ)
• HotpotQA
• 2Wiki MultiHopQA

Training Procedure

DeepResearcher was trained using reinforcement learning (RL) with the Group Relative Policy Optimization (GRPO) algorithm. It was tested in both in-domain (NQ, TQ, HotpotQA) and out-of-domain (Musique, Bamboogle, PopQA) settings .

Evaluation

Testing Data

The model was evaluated on several datasets, including:

• NQ (Natural Questions)
• TQ (TriviaQA)
• HotpotQA
• 2Wiki
• Musique
• Bamboogle
• PopQA .

Results

DeepResearcher outperforms all baseline models, achieving a substantial improvement in task completion across the datasets, particularly in out-of-domain scenarios.

Citation

@misc{zheng2025deepresearcherscalingdeepresearch,
      title={DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments}, 
      author={Yuxiang Zheng and Dayuan Fu and Xiangkun Hu and Xiaojie Cai and Lyumanshan Ye and Pengrui Lu and Pengfei Liu},
      year={2025},
      eprint={2504.03160},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2504.03160}, 
}