Update README.md

README.md

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-license: apache-2.0
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+# 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.
+