Instruction
ASearcher is an open-source framework designed for large-scale online reinforcement learning (RL) training of search agents. Our mission is to advance Search Intelligence to expert-level performance. We are fully committed to open-source by releasing model weights, detailed training methodologies, and data construction pipelines. Additionally, we provide comprehensive guidance on building and training customized agents based on AReaL. ASearcher empowers developers to build their own high-performance search agents easily and cost-effectively.
We have released multiple models trained with different settings and based on foundation models of varying sizes. These models have achieved outstanding performance on Single-Hop / Multi-Hop QA and more challenging tool-augmented benchmarks like GAIA, Xbench.
Model Download
| Model Name | Base Model | Training Setting | Download Link |
|---|---|---|---|
| ASearcher-Local-7B | Qwen2.5-7B | Local knowledge base with RAG | 🤗Huggingface |
| ASearcher-Web-7B | Qwen2.5-7B | Web-based search and browsing | 🤗Huggingface |
| ASearcher-Local-14B | Qwen2.5-14B | Local knowledge base with RAG | 🤗Huggingface |
| ASearcher-Web-14B | Qwen2.5-14B | Web-based search and browsing | 🤗Huggingface |
| ASearcher-Web-QwQ-32B | QwQ-32B | Web-based search and browsing | 🤗Huggingface |
Performance
Evaluation on challenging benchmarks (ASearcher-Web-QwQ)
Evaluation with a local knowledge base with RAG
Evaluation with web-based search and browsing
Dataset Download
We also release our full training data and test data, you can easily get them and reproduce our result.
Quickstart
If you want to learn more details, please refer to our GitHub repository: ASearcher