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---
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license: apache-2.0
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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---
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# GAIR/DeepResearcher-7b
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## Introduction
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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.
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## Model Details
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- **License:** Apache 2.0
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- **Model type:** Reinforcement learning-based LLM (Large Language Model).
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- **Language(s):** The model is designed for tasks in English.
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- **Finetuned from model:** The model is built using the Qwen2.5-7B-Instruct architecture .
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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### Model Sources
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- **Repository:** [DeepResearcher GitHub](https://github.com/GAIR-NLP/DeepResearcher) .
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- **Paper:** [DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments](https://arxiv.org/abs/2504.03160)
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## How to Get Started with the Model
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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 .
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## Training Details
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### Training Data
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The model was trained on open-domain question-answering datasets, including:
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- **NaturalQuestions (NQ)**
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- **TriviaQA (TQ)**
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- **HotpotQA**
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- **2Wiki MultiHopQA**
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### Training Procedure
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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 .
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## Evaluation
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### Testing Data
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The model was evaluated on several datasets, including:
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- **NQ (Natural Questions)**
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- **TQ (TriviaQA)**
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- **HotpotQA**
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- **2Wiki**
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- **Musique**
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- **Bamboogle**
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- **PopQA** .
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### Results
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DeepResearcher outperforms all baseline models, achieving a substantial improvement in task completion across the datasets, particularly in out-of-domain scenarios.
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## Citation
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```
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@misc{zheng2025deepresearcherscalingdeepresearch,
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title={DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments},
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author={Yuxiang Zheng and Dayuan Fu and Xiangkun Hu and Xiaojie Cai and Lyumanshan Ye and Pengrui Lu and Pengfei Liu},
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year={2025},
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eprint={2504.03160},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={https://arxiv.org/abs/2504.03160},
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}
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```
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