WorkFactory：将专业意图转化为可执行任务，用于可扩展的智能体训练

Authors:Ziyang Zeng, Tingyang Chen, Jia Deng, Qiyao Wang, Shuo Tang, Yike Yang, Tao Ren, Wayne Xin Zhao, Feng Chang, Jujie He, Chuan Hao, Ran Tao, Bryan Dai

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Abstract:Scaling supervised training for working agents requires diverse professional tasks that yield reliable expert demonstrations. However, missing or inconsistent task information can obstruct teacher execution and weaken the resulting supervision. We propose WorkFactory, a scalable framework for constructing coherent and executable professional tasks. Starting from professional intents, information-level planning builds an information support graph to guide task instantiation, explicitly representing the dependencies and locations of information required for the intended outputs. Probe executions then identify support failures and guide repairs while preserving the intended work. We construct 112,686 tasks spanning 612 occupations and use the collected teacher trajectories to fine-tune Qwen models at 4B, 27B, and 35B-A3B scales, yielding the WorkPilot family. All three models improve over their respective base models across four professional-work benchmarks, with WorkPilot-35B-A3B improving from 879 to 1565 Elo on GDPval-AA v2.1. Scaling analyses reveal model-dependent diminishing returns, with execution length first increasing and then decreasing. Controlled component analyses show that planning and repair reduce probe-diagnosed task environment defects and improve downstream performance. The data and models are available at this https URL.

Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2610.37161 [cs.AI]
	(or arXiv:2610.37161v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.37161 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Tingyang Chen [view email]
[v1] Tue, 27 Oct 2026 12:54:16 UTC (816 KB)