README.md

---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
---
# 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

<!-- Provide a longer summary of what this model is. -->


### Model Sources

- **Repository:** [DeepResearcher GitHub](https://github.com/GAIR-NLP/DeepResearcher) .
- **Paper:** [DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments](https://arxiv.org/abs/2504.03160)


## How to Get Started with the Model

To get started, you can visit the [DeepResearcher repository](https://github.com/GAIR-NLP/DeepResearcher) 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}, 
}
```