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GAIR-NLP / DeepResearcher Public Notifications You must be signed in to change notification settings Fork 54 Star 772 Code Issues 9 Pull requests 0 Actions Projects Security and quality 0 Insights Additional navigation options Code Issues Pull requests Actions Projects Security and quality Insights GAIR-NLP/DeepResearcher main Branches Tags Go to file Code Open more actions menu Folders and files Name Name Last commit message Last commit date Latest commit History 18 Commits 18 Commits data data docker docker docs docs evaluate evaluate examples examples images images patches patches resources resources scripts scripts scrl scrl signal signal tests tests verl verl .gitignore .gitignore LICENSE LICENSE Notice.txt Notice.txt README.md README.md VERL_README.md VERL_README.md evaluate.sh evaluate.sh pyproject.toml pyproject.toml requirements.txt requirements.txt setup.py setup.py train_grpo.sh train_grpo.sh View all files Repository files navigation README Apache-2.0 license More items DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments This is the official repository for DeepResearcher . 📝 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. 📋 Table of Contents Introduction Model Performance Get started Acknowledgement Citation 🤖 Model DeepResearcher is now available on huggingface-hub: Model Name HF Checkpoint Size DeepResearcher-7b 🤗 GAIR/DeepResearcher-7b 7B 🏆 Performance Extensive experiments on open-domain research tasks demonstrate that DeepResearcher achieves substantial improvements of up to 28.9 points over prompt engineering-based baselines and up to 7.2 points over RAG-based RL agents. 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. Our results highlight that end-to-end training in real-world web environments is not merely an implementation detail but a fundamental requirement for developing robust research capabilities aligned with real-world applications. 🚀 Get Started Package Installation To begin using this repo, you need to install the required dependencies. You can do this by running the following command: git clone https://github.com/GAIR-NLP/DeepResearcher.git conda create -n deepresearcher python=3.10 conda activate deepresearcher cd DeepResearcher pip3 install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu124 pip3 install flash-attn --no-build-isolation pip3 install -e . pip3 install -r requirements.txt Start ray before training and inference We use ray to train model, befor start ray you should set PET_NODE_RANK first. ( This is compulsory even if you only have 1 node ). Here is the code of the head node: export PET_NODE_RANK=0 ray start --head Run backend handler Running the following command to launch the server handler: Modify serper_api_key or azure_bing_search_subscription_key & search_engine in ./scrl/handler/config.yaml Add qwen-plus api key in ./scrl/handler/server_handler.py client = OpenAI ( api_key = "sk-xxx" , base_url = "xxxx" ) Start server handler: python ./scrl/handler/server_handler.py After launching all server handlers, you can replace server_url_list in ./scrl/handler/config.yaml in your training host node and then run: python ./scrl/handler/handler.py Training model Using the following command to train the model: bash train_grpo.sh Evaluate Using the following command to generate rollout: bash evaluate.sh You can find the rollout file in: ./outputs/{project_name}/{experiment_name}/rollout/rollout_step_0.json You can rename and copy it into ./evaluate/{experiment_name}_result.json Then, run the following command: python ./evaluate/cacluate_metrics.py {experiment_name} You can check the score in ./evaluate/{experiment_name}_score.json 🙏 Acknowledgement DeepResearcher is inspired by Deepseek-R1 with its implementation based on veRL and Search-r1 . We deeply appreciate the contributions of these teams to open-source research and development. ✍️ Citation Please cite the repo if the model/code/conclusion in this repo are helpful to you. @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}, } About Scaling Deep Research via Reinforcement Learning in Real-world Environments. Resources Readme License Apache-2.0 license Uh oh! There was an error while loading. Please reload this page . Activity Custom properties Stars 772 stars Watchers 7 watching Forks 54 forks Report repository Releases No releases published Packages 0 Uh oh! There was an error while loading. Please reload this page . Uh oh! 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