File size: 2,707 Bytes
6af204d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | # 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.
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