ZeroSearch - Ali Tongyi launched the open source large model search engine framework

What is ZeroSearch

ZeroSearch is Alibaba Tongyi Labs open source innovative large model search engine framework. The framework does not need to interact with the real search engine , based on the simulation of the search engine , with a large model of its own pre-training knowledge to generate relevant or noisy documents , significantly reducing the training cost (reduce 80% or more).ZeroSearch based on lightweight supervisory fine-tuning and course learning mechanisms to gradually improve the model's reasoning ability , support for a variety of reinforcement learning algorithms (such as PPO, GRPO). ZeroSearch has excellent performance on multiple Q&A datasets and outperforms Google search.ZeroSearch is applicable to a variety of scenarios such as intelligent Q&A, content creation, and research and development, and is highly scalable and generalizable.

Key Features of ZeroSearch

• Analog search capability: ZeroSearch can simulate the search function of a search engine and generate documents with the knowledge reserve of the big model itself, without relying on external real search engines, reducing the cost of use and external dependence.
• Flexible Document Generation: It supports generating high-quality documents related to the query or generating noisy documents, flexibly controlling the quality of documents based on adjusting the cue words, and providing diversified retrieval scenarios for model training.
• Efficient cost reduction: ZeroSearch dramatically reduces training costs and makes large-scale training more economically viable than using real search engines for reinforcement learning training.
• high compatibility: It is compatible with many large models with different parameter scales (e.g., 3B, 7B, 14B), supports many reinforcement learning algorithms (e.g., PPO, GRPO), and is highly scalable and generalizable.

ZeroSearch's official website address

• Project website::https://alibaba-nlp.github.io/ZeroSearch/
• GitHub repository::https://github.com/Alibaba-nlp/ZeroSearch
• HuggingFace Model Library::https://huggingface.co/collections/sunhaonlp/zerosearch
• arXiv Technical Paper::https://arxiv.org/pdf/2505.04588

How to use ZeroSearch

• environmental preparation::
  • Installing Python: Ensure that Python is installed on your system (Python 3.8 and above is recommended).
  • Installation of dependent libraries: Install the necessary Python libraries as required by ZeroSearch. This can usually be done with the following commands:

pip install -r requirements.txt

• • The specific dependency files can be found in the GitHub repository.
• Getting the code and the model::
  • Cloning GitHub Repositories: Clone the code from ZeroSearch's official GitHub repository:

git clone https://github.com/Alibaba-nlp/ZeroSearch.git
cd ZeroSearch

• • Download pre-trained model: Download the required pre-trained model files according to ZeroSearch's instructions.
• Configuration environment::
  • Configuring Model Paths: Specify the path to the pre-trained model in the code to ensure that ZeroSearch loads the model correctly.
  • Setting parameters: Adjust parameters in ZeroSearch's configuration file or code as needed, such as model size, reinforcement learning algorithms, training data paths, and so on.
• Run ZeroSearch::
  • priming training: Run the ZeroSearch training script. Start it based on the following command:

python train.py

• • Specific script names and parameters may vary from version to version, please refer to the official documentation.
• Testing and validation: After training is complete, the performance of ZeroSearch is verified with a test dataset to ensure that relevant documents are correctly generated and questions are answered.

ZeroSearch's Core Benefits

• No real search engine interaction required: ZeroSearch is based on simulated search engine functionality and is completely independent of external search engines, reducing costs and dependencies.
• Significant cost reductions: Compared to traditional methods, ZeroSearch's training cost is dramatically reduced, making large-scale training more cost-effective.
• Flexible document generation capabilitiesThe program supports the generation of high-quality or noisy documents, which can be flexibly adjusted according to the user's needs to meet diversified training scenarios.
• Powerful technical realization: Improving model performance and inference based on lightweight supervised fine-tuning, course-learning mechanisms, and reward mechanisms based on F1 scores.
• Wide range of applicabilityIt is compatible with a variety of large models and reinforcement learning algorithms, and is suitable for multiple scenarios such as intelligent Q&A, content creation, education, and enterprise knowledge management.
• Open Source and Community Support: As an open source framework, ZeroSearch provides code free access and community support for easy customization and optimization.

Application Scenarios for ZeroSearch

• Artificial intelligence researchers: Model training and algorithm optimization based on an efficient and low-cost search framework.
• natural language processing developer: Rapidly build applications in areas such as smart Q&A and content creation.
• Corporate Technical Team: Technicians optimize enterprise knowledge management and improve internal search efficiency.
• Educators and students: Used in online education and smart tutoring to provide instant answers and learning support.
• content creator: Content creation to access information, generate first drafts or inspiration, and improve creative efficiency.
• Open Source Community Enthusiasts: Interested in open source projects and want to contribute or do secondary development.