@inproceedings{hao-etal-2026-hardgen,
title = "{H}ard{G}en: Generating Hard Samples for Tool-use Agents",
author = "Hao, Bingguang and
Xu, Zengzhuang and
Wen, Yuntao and
Xu, Xinyi and
Sun, Quanen and
Liu, Yang and
Huang, Zihao and
Zhao, Tong and
Chen, Long and
Wang, Dong and
Chen, Yicheng and
Wang, Maolin and
Peng, Cunyin and
Zhao, Xiangyu and
Zhuang, Chenyi and
Zhang, Ji",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.285/",
pages = "6520--6546",
ISBN = "979-8-89176-459-0",
abstract = "The advancement of LLM agents with tool-use capabilities requires diverse and complex training corpora. Existing data generation methods, which predominantly follow the paradigm of random sampling and shallow generation, often yield simple and homogeneous trajectories that fail to capture complex, implicit logical dependencies. To bridge this gap, we introduce **HardGen**, an automatic agentic pipeline designed to generate hard tool-use training samples. First, HardGen establishes a dynamic API Graph built upon agent failure cases, from which it samples hard tool-execution sequences. Second, these execution sequences are used to reversely build advanced tools that combine multi-step operations into one, which then help generate hard queries requiring unstated intermediate steps. Finally, advanced tools and hard queries drive the generation of Chain-of-Thought (CoT), which is iteratively refined through closed-loop verification feedback. Extensive evaluations demonstrate that a 4B parameter model trained with our curated dataset achieves superior performance compared to several leading open-source and closed-source competitors (e.g., GPT-5.2, Gemini-3-Pro and Claude-Opus-4.5). Our code, models and the curated dataset with 27,000 samples will be open-sourced to facilitate future research."
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<abstract>The advancement of LLM agents with tool-use capabilities requires diverse and complex training corpora. Existing data generation methods, which predominantly follow the paradigm of random sampling and shallow generation, often yield simple and homogeneous trajectories that fail to capture complex, implicit logical dependencies. To bridge this gap, we introduce **HardGen**, an automatic agentic pipeline designed to generate hard tool-use training samples. First, HardGen establishes a dynamic API Graph built upon agent failure cases, from which it samples hard tool-execution sequences. Second, these execution sequences are used to reversely build advanced tools that combine multi-step operations into one, which then help generate hard queries requiring unstated intermediate steps. Finally, advanced tools and hard queries drive the generation of Chain-of-Thought (CoT), which is iteratively refined through closed-loop verification feedback. Extensive evaluations demonstrate that a 4B parameter model trained with our curated dataset achieves superior performance compared to several leading open-source and closed-source competitors (e.g., GPT-5.2, Gemini-3-Pro and Claude-Opus-4.5). Our code, models and the curated dataset with 27,000 samples will be open-sourced to facilitate future research.</abstract>
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%0 Conference Proceedings
%T HardGen: Generating Hard Samples for Tool-use Agents
%A Hao, Bingguang
%A Xu, Zengzhuang
%A Wen, Yuntao
%A Xu, Xinyi
%A Sun, Quanen
%A Liu, Yang
%A Huang, Zihao
%A Zhao, Tong
%A Chen, Long
%A Wang, Dong
%A Chen, Yicheng
%A Wang, Maolin
%A Peng, Cunyin
%A Zhao, Xiangyu
%A Zhuang, Chenyi
%A Zhang, Ji
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hao-etal-2026-hardgen
%X The advancement of LLM agents with tool-use capabilities requires diverse and complex training corpora. Existing data generation methods, which predominantly follow the paradigm of random sampling and shallow generation, often yield simple and homogeneous trajectories that fail to capture complex, implicit logical dependencies. To bridge this gap, we introduce **HardGen**, an automatic agentic pipeline designed to generate hard tool-use training samples. First, HardGen establishes a dynamic API Graph built upon agent failure cases, from which it samples hard tool-execution sequences. Second, these execution sequences are used to reversely build advanced tools that combine multi-step operations into one, which then help generate hard queries requiring unstated intermediate steps. Finally, advanced tools and hard queries drive the generation of Chain-of-Thought (CoT), which is iteratively refined through closed-loop verification feedback. Extensive evaluations demonstrate that a 4B parameter model trained with our curated dataset achieves superior performance compared to several leading open-source and closed-source competitors (e.g., GPT-5.2, Gemini-3-Pro and Claude-Opus-4.5). Our code, models and the curated dataset with 27,000 samples will be open-sourced to facilitate future research.
%U https://aclanthology.org/2026.emnlp-main.285/
%P 6520-6546
Markdown (Informal)
[HardGen: Generating Hard Samples for Tool-use Agents](https://aclanthology.org/2026.emnlp-main.285/) (Hao et al., EMNLP 2026)
ACL
- Bingguang Hao, Zengzhuang Xu, Yuntao Wen, Xinyi Xu, Quanen Sun, Yang Liu, Zihao Huang, Tong Zhao, Long Chen, Dong Wang, Yicheng Chen, Maolin Wang, Cunyin Peng, Xiangyu Zhao, Chenyi Zhuang, and Ji Zhang. 2026. HardGen: Generating Hard Samples for Tool-use Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 6520–6546, Budapest, Hungary. Association for Computational Linguistics.