inclusionAI/ASearcher-Web-QwQ

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