\section{Related Work} \label{sec:related_work}

\noindent \textbf{LLM-based Multi-Agent Systems.}
Recent work has explored architectures where multiple LLM-based agents cooperate, each with specialized roles, to tackle complex tasks beyond the ability of a single model~\citep{hyperagent,agentnet,guo2024large,chen2023agentverse,trivedi2024appworld}.
Early two-agent role-play systems such as CAMEL~\citep{camel} showed that dialog between agents can elicit step-wise reasoning.
Recent frameworks extend this idea by assigning explicit roles to multiple agents: MetaGPT~\citep{metagpt} and ChatDev~\citep{chatdev} replicate software-engineering pipelines with manager, designer, and coder agents;
% \textsc{AgentNet} decentralises coordination through evolutionary self-organisation (Yang et al., 2025); 
Other works like Magnetic-One~\citep{fourney2024magentic} and AG2~\citep{ag2} introduce a central orchestrator that assign tasks to workers.  
While effective, these systems hard-code role sets, limiting cross-domain transfer. 
In~\cite{why-fail}, the authors find that major failure pattern of those works stem from \emph{system design issues}.
\workforce instead decouples strategic planning, coordination, and task execution into independent agents, so new workers can be swapped in without touching the planner. The scalability and flexibility enables seamless domain transfer.

\noindent \textbf{Post-Training for Agentic LLMs.}
Researchers have extensively explored post-training methods to enhance the performance of agentic systems. 
Prior works optimize the model on tool-augmented trajectories by supervised fine-tuning~\citep{chen2024agent,chen2023fireact,zeng2023agenttuning,hu2024agentgen,qin2023toolllm} or reinforcement learning~\citep{webrl,steptool,agentic,swe-rl,sweet-rl,jin2025search,zheng2025deepresearcher,jin2025search,feng2025retool} 
% WebRL~\citep{webrl}, StepTool~\citep{steptool}, Agentic Reasoning~\citep{agentic}, AgentLess~\citep{agentless}, SWE-RL~\citep{swe-rl}, SWEET-RL~\citep{sweet-rl}
Although effective, full-trajectory training scales poorly and can over-fit the agent to a fixed tool suite.  
Another coccurent work MPO~\citep{mpo} sidesteps weight updates by iteratively refining high-level 
plans but still assumes task-specific reward design.  
In contrast, \textsc{OWL} trains \emph {only} the domain-agnostic planner via reinforcement learning, enabling strong domain transferability.
% of \workforce-based multi-agent systems.

\noindent \textbf{Multi-Agent Training.}
Recent research has explored training methods where multiple LLM-based agents learn to collaborate through specialized roles and interactions~\citep{ma2024coevolving,fama,debategpt}.
For example, MALT divides the reasoning process among a generator, verifier, and refiner agent, and fine-tunes each role with off-policy reward propagation\citep{malt}. 
Similarly,~\cite{subramaniam2025multiagent} propose a multiagent fine-tuning approach where LLMs initialized from the same base model are specialized through debate-driven data, enabling diverse reasoning and collective self-improvement beyond single-agent capabilities.
Unlike these approaches that train domain-specific multi-agent systems where each agent requires separate fine-tuning, \textsc{OWL} focuses on training a single generalizable domain-agnostic planner. This fundamental difference offers both effective domain transferability and training efficiency. 

