# 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:

```bash
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:

```bibtex
@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](https://github.com/inclusionAI/ASearcher) or open an issue on the project page.