ASearcher-Web-QwQ-V2

Overview

ASearcher-Web-QwQ-V2 is a 32B-scale search agent trained using large-scale reinforcement learning. This model represents an improved version of the ASearcher framework, achieving cutting-edge performance on challenging web search benchmarks through advanced agentic RL training techniques.

Key Features

🏆 Cutting-Edge Performance: Achieves Avg@4 scores of 58.7, 51.1, and 74.5 on GAIA, xBench-DeepSearch, and Frames benchmarks respectively
⚡ Fully Asynchronous RL Training: Enables efficient long-horizon search capabilities with tool calls exceedind 100 rounds
🔁 Advanced Data Synthesis: Trained on autonomously generated QA pairs with rigorous multi-stage validation
🌐 Real Web Search Capabilities: Designed to interact with live web search tools for up-to-date information retrieval

Performance Highlights

Benchmark	Avg@4 Score	Pass@4 Score
GAIA	58.7	74.7
xBench-DeepSearch	51.1	75.0
Frames	74.5	85.5

Substantial RL Improvements: Reinforcement learning training brings significant gains:

+15.0 improvement on GAIA
+22.4 improvement on xBench-DeepSearch
+14.6 improvement on Frames

Quick Start

Evaluation

To reproduce the benchmark results:

cd evaluation/
python search_eval_async.py \
    --model_name_or_path inclusionAI/ASearcher-Web-QwQ-V2 \
    --data_names GAIA,xbench-deepsearch,Frames \
    --agent-type asearcher-reasoning \
    --search-client-type async-web-search-access

Training Details

This model was trained using:

Architecture: QwQ-32B
Training Method: Fully asynchronous reinforcement learning
Data: Synthesized QA pairs with multi-stage validation
Framework: AReaL

Applications

Complex web search and information retrieval
Multi-step problem solving with tool usage
Real-time information gathering and synthesis
Long-horizon reasoning tasks

Citation

If you use this model, please cite:

@misc{gao2025turnsunlockinglonghorizonagentic,
      title={Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL}, 
      author={Jiaxuan Gao and Wei Fu and Minyang Xie and Shusheng Xu and Chuyi He and Zhiyu Mei and Banghua Zhu and Yi Wu},
      year={2025},
      eprint={2508.07976},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.07976}, 
}

License

license: apache-2.0

Contact

For questions and support, please refer to the ASearcher GitHub repository or open an issue on the project page.