Poster Thu, Apr 23, 2026 • 11:15 AM – 1:45 PM PDT Pavilion 3 P3-#1403

TaskCraft: Automated Generation of Agentic Tasks

Dingfeng Shi ⋅ Jingyi Cao ⋅ Qianben Chen ⋅ Weichen Sun ⋅ Weizhen Li ⋅ Hongxuan Lu ⋅ Fangchen Dong ⋅ Tianrui Qin ⋅ Zhu ⋅ Minghao Liu ⋅ Yuchen Jiang ⋅ Jian Yang ⋅ Ge Zhang ⋅ JIAHENG LIU ⋅ Changwang Zhang ⋅ Jun Wang ⋅ Wangchunshu Zhou

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Abstract

Agentic tasks, which require multistep problem solving with tool use and adaptive reasoning, are becoming increasingly central to the advancement of NLP and AI. Although benchmarks such as GAIA and BrowseComp have advanced agent evaluation, their scalability remains limited by the high cost of human annotation. We introduce TaskCraft, the first automated workflow for generating scalable, multitool, and verifiable agentic tasks of difficulty. TaskCraft progressively complexifies atomic tasks through depth-based and width-based extensions, with incremental validation via rejection sampling and LLM-based linguistic analysis, ensuring both scalability and efficiency. The generated tasks enable trajectory sampling within state-of-the-art workflows, supporting end-to-end SFT and RL training. Experimental results on multiple LLMs show that TaskCraft data substantially improves multi-hop reasoning and agentic capabilities. Further scaling with TaskCraft tasks and applying RL training yields additional gains, achieving state-of-the-art performance on four agentic benchmarks. The resulting dataset comprises 41k tool-intensive tasks across varied difficulty levels, including 12.6k tool-interaction trajectories and 5k multihop decompositions.