IQuestLab Releases WorkFactory Framework: Enhancing Agent Training with Scalable Professional Task Construction
Summary:IQuestLab has introduced the WorkFactory framework to address the challenge of missing or inconsistent task information in agent training. This scalable framework constructs an information support graph to guide task instantiation and employs probe executions to identify and repair task defects, resulting in coherent and executable professional tasks. WorkFactory has generated 112,686 tasks across 612 occupations and fine-tuned Qwen models to create the WorkPilot family. Benchmarks demonstrate t
Technical Mechanism Analysis
The core of the WorkFactory framework lies in its information-level planning and probe execution mechanisms. Starting from professional intents, WorkFactory constructs an information support graph that explicitly represents the dependencies and locations of information required for the intended outputs. This process ensures the coherence of task instantiation and avoids missing or conflicting task information. Subsequently, the probe execution mechanism simulates task execution, identifies potential support failures, and guides the repair process while preserving the intended goals of the tasks. This approach not only enhances task reliability but also improves the quality of the training data for agents.
Engineering Trade-offs and Performance
The main challenge in building the WorkFactory framework is balancing task complexity with scalability. By adopting a modular design and efficient graph algorithms, WorkFactory supports large-scale task sets while maintaining low computational overhead. Experimental results show that WorkFactory has generated 112,686 tasks across 612 occupations, significantly improving the performance of Qwen models across four professional work benchmarks. For instance, the WorkPilot-35B-A3B model increased its Elo score from 879 to 1565 on the GDPval-AA v2.1 benchmark, demonstrating the framework's great potential in enhancing agent training efficiency.
Developer Implementation and Deployment Recommendations
For developers, the openness of the WorkFactory framework makes it easy to integrate into existing agent training workflows. It is recommended that developers leverage WorkFactory to build task sets for specific domains based on their task requirements and further optimize model performance using fine-tuning techniques. Additionally, developers should focus on the quality control of task instantiation and ensure the effectiveness of the probe execution mechanism to fully realize the advantages of WorkFactory.
Conclusion
The release of the WorkFactory framework marks a significant advancement in the field of agent training. By providing high-quality, executable professional tasks, WorkFactory not only enhances the performance of agent models but also lays a solid foundation for the application of AI in professional fields.
— END —Source: Hugging Face Daily Papers (2026-10-27)
Tags: #IQuestLab #WorkFactory #Agent Training #Qwen Models #Professional Tasks
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