OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation

Mengkang Hu, Yuhang Zhou, Wendong Fan, Yuzhou Nie, Ziyu Ye, Bowei Xia, Tao Sun, Zhaoxuan Jin, Yingru Li, Zeyu Zhang, Yifeng Wang, Qianshuo Ye, Bernard Ghanem, Ping Luo, Guohao Li

Keywords: Large Language Model, LLM-based Agent, Multi-Agent

Abstract:

Large Language Model (LLM)-based multi-agent systems show promise for automating real-world tasks but struggle to transfer across domains due to their domain-specific nature.
Current approaches face two critical shortcomings: they require complete architectural redesign and full retraining of all components when applied to new domains.
We introduce **Workforce**, a hierarchical multi-agent framework that decouples strategic planning from specialized execution through a modular architecture comprising:
*(i)* a *domain-agnostic* **Planner** for task decomposition,
*(ii)* a **Coordinator** for subtask management, and
*(iii)* specialized **Workers** with *domain-specific* tool-calling capabilities.
This decoupling enables cross-domain transferability during both inference and training phases:
During inference, Workforce seamlessly adapts to new domains by adding or modifying worker agents;
For training, we introduce **Optimized Workforce Learning (OWL)**, which improves generalization across domains by optimizing a domain-agnostic planner with reinforcement learning from real-world feedback.
To validate our approach, we evaluate Workforce on the GAIA benchmark, covering various realistic, multi-domain agentic tasks.
Experimental results demonstrate Workforce achieves open-source state-of-the-art performance (**69.70%**), outperforming commercial systems like OpenAI's Deep Research by **2.34%**.
More notably, our OWL-trained 32B model achieves **52.73%** accuracy (**+16.37%**) and demonstrates performance comparable to GPT-4o on challenging tasks.
To summarize, by enabling scalable generalization and modular domain transfer, our work establishes a foundation for the next generation of general-purpose AI assistants.

*Our code is available at [Anonymous URL](https://anonymous.4open.science/r/annonymous-owl/), and our data is available at [Anonymous URL](https://huggingface.co/anonymous21016).*

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 21016