WorkFactory：通过信息规划与修复将专业意图转化为可执行任务以实现可扩展智能体训练
WorkFactory: Turning Professional Intents into Executable Work for Scalable Agent Training
WorkFactory通过信息规划与修复构建连贯的专业任务，产出WorkPilot模型，在专业工作基准测试中提升专家级智能体表现。该框架从专业意图出发，构建信息支持图指导任务实例化，并通过探测执行识别支持失败并指导修复，同时保留预期工作。
Published on Oct 27
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Abstract
WorkFactory builds coherent professional tasks via information planning and repair, yielding WorkPilot models that improve expert-level agent performance across benchmarks.
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 https://github.com/IQuestLab/WorkFactory.
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