Duplicate from Bingguang/HardGen

Co-authored-by: Bingguang Hao <Bingguang@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+task_categories:
+- question-answering
+- text-generation
+language:
+- en
+tags:
+- agent
+- Agentic Learning
+- tool use
+- BFCL
+size_categories:
+- 10K<n<100K
+---
+
+
+# From Failure to Mastery: Generating Hard Samples for Tool-use Agents
+
+
+[![arXiv](https://img.shields.io/badge/arXiv-2601.01498-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2601.01498) [![arXiv](https://img.shields.io/badge/arXiv-2510.24645-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2510.24645) [![Model](https://img.shields.io/badge/Hugging%20Face-Model-yellow?logo=huggingface)](https://huggingface.co/Bingguang/FunReason-MT) [![GitHub](https://img.shields.io/badge/GitHub-Code-181717?logo=github)](https://github.com/inclusionAI/AWorld-RL) [![Project Page](https://img.shields.io/badge/Project-AWorld-green)](https://github.com/inclusionAI/AWorld)
+
+***
+
+
+> [!IMPORTANT] Important Hint
+
+> - This is an extension of the technical report **FunReason-MT Technical Report: Advanced Data Synthesis Solution for Real-world Multi-Turn Tool-use**
+> - To allow the model to learn from errors, we specifically construct erroneous environmental responses. If you wish to delete this data, please delete the trajectories where `error_tool_response` is true.
+> - This is an initial version of our data, we will release the up-to-date version after the acceptance of our paper.
+
+
+## Dataset
+
+The training set comprises **16,000 high-quality multi-turn samples**. This dataset was generated using the three-phase HardGen (FunReason-MT) data synthesis framework, which focuses on generating complex trajectories.
+
+
+## 📊 HardGen Evaluation Results
+
+The model built upon HardGen is rigorously evaluated on the Berkeley Function-Calling Leaderboard (BFCL).
+
+### BFCLv3 Multi-Turn and Single-Turn Performance
+
+| Model (4B - 235B)                      |             Multi-Turn (Overall)             |            Single-Turn (Overall)            |
+| :------------------------------------- | :------------------------------------------: | :------------------------------------------: |
+| Qwen3-4B-Instruct (Base)               |        22.13         |        82.14         |
+| **Qwen3-4B + HardGen (RL)** | **63.13**  | **87.14**  |
+| Gemini-3-Pro-Preview               |         60.75         |        86.89         |
+| DeepSeek-V3.2-Exp                       |        44.88         |        80.77         |
+| GPT-5.2-2025-12-11                      |        28.13        |        76.12         |
+
+### BFCL Agentic Evaluation (BFCLv4 OOD)
+
+The performance of models trained upon Llama-3.1-8B-Instruct on agentic tasks (Web Search and Memory).
+
+| Model                          |             BFCLv4 Overall Score             |
+| :----------------------------- | :------------------------------------------: |
+| **HardGen-8B (RL)** | **20.42**  |
+|CoALM-8B | 1.40|
+|ToolACE-2-8B | 13.50|
+|BitAgent-8B | 8.24|
+|xLAM-2-8b-fc-r | 10.24|
+-----
+
+### Training Details
+
+  - **Training Libraries:** LLama-Factory and Verl.
+  - **Methodology:** Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL).
+  - **Hardware:** Conducted on 8 NVIDIA A100 GPUs.
+
+-----
+
+## 🔗 Related Projects and Citation
+
+This work is part of the open-source project **[AWorld, InclusionAI](https://github.com/inclusionAI/AWorld/)**.
+
+If you use this dataset in your research, please cite the technical report:
+
+```
+@article{hao2026failure,
+  title={From Failure to Mastery: Generating Hard Samples for Tool-use Agents},
+  author={Hao, Bingguang and Xu, Zengzhuang and Wen, Yuntao and Xu, Xinyi and Liu, Yang and Zhao, Tong and Wang, Maolin and Chen, Long and Wang, Dong and Chen, Yicheng and others},
+  journal={arXiv preprint arXiv:2601.01498},
+  year={2026}
+}
+@article{xu2025funreason,
+  title={FunReason-MT Technical Report: Advanced Data Synthesis Solution for Real-world Multi-Turn Tool-use},
+  author={Zengzhuang Xu, Bingguang Hao, Zechuan Wang, Yuntao Wen, Xinyi Xu, Yang Liu, Long Chen, Dong Wang, Maolin Wang, Tong Zhao, Yicheng Chen, Cunyin Peng, Jinjie Gu, Leilei Gan, Xiangyu Zhao, Chenyi Zhuang, Shi Gu},
+  journal={arXiv preprint arXiv:2510.24645},
+  year={2025}
+}
+```
+### Contact
+
+For inquiries, please contact:
+
+* `bingguanghao7@gmail.com`

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