TaskCraft: Automated Generation of Agentic Tasks
Agentic tasks, which require multi-step problem solving with autonomy, tool
use, and adaptive reasoning, are becoming increasingly central to the
advancement of NLP and AI. However, existing instruction data lacks tool
interaction, and current agentic benchmarks rely on costly human annotation,
limiting their scalability. We introduce TaskCraft, an automated
workflow for generating difficulty-scalable, multi-tool, and verifiable agentic
tasks with execution trajectories. TaskCraft expands atomic tasks using
depth-based and width-based extensions to create structurally and
hierarchically complex challenges. Empirical results show that these tasks
improve prompt optimization in the generation workflow and enhance supervised
fine-tuning of agentic foundation models. We present a large-scale synthetic
dataset of approximately 36,000 tasks with varying difficulty to support future
research on agent tuning and evaluation.
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Dingfeng Shi
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Jingyi Cao
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Qianben Chen
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2025-07-24T09:13:48.082Z
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Chen
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Qianben
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Weichen Sun
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Weizhen Li
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Hongxuan Lu
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Fangchen Dong
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Tianrui Qin
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2025-06-24T08:10:47.132Z
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tianrui
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tianyue818
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King Zhu
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Minghao Yang
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Jian Yang
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Ge Zhang
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2025-06-18T12:16:56.535Z
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Ge Zhang
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zhangysk
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Jiaheng Liu
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Changwang Zhang
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2025-09-06T10:57:47.796Z
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Changwang ZHANG
user
mLeoKing
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Jun Wang
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Yuchen Eleanor Jiang
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Wangchunshu Zhou
claimed_verified
2025-06-18T12:16:54.076Z
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Zhou
user
Wangchunshu
TaskCraft: Automated Generation of Agentic Tasks
Agentic tasks, which require multi-step problem solving with autonomy, tool
use, and adaptive reasoning, are becoming increasingly central to the
advancement of NLP and AI. However, existing instruction data lacks tool
interaction, and current agentic benchmarks rely on costly human annotation,
limiting their scalability. We introduce TaskCraft, an automated
workflow for generating difficulty-scalable, multi-tool, and verifiable agentic
tasks with execution trajectories. TaskCraft expands atomic tasks using
depth-based and width-based extensions to create structurally and
hierarchically complex challenges. Empirical results show that these tasks
improve prompt optimization in the generation workflow and enhance supervised
fine-tuning of agentic foundation models. We present a large-scale synthetic
dataset of approximately 36,000 tasks with varying difficulty to support future
research on agent tuning and evaluation.
TaskCraft automates the generation of scalable, multi-tool, and complex agentic tasks to enhance prompt optimization and fine-tuning of agentic models.
agentic tasks multi-step problem solving tool use adaptive reasoning instruction data human annotation difficulty-scalable multi-tool verifiable tasks execution trajectories depth-based extensions width-based extensions hierarchical complexity prompt optimization supervised fine-tuning agentic foundation models large-scale synthetic dataset