MoRSE: Task-Oriented Multi-Agent System with Mixture of Role-Subtask Experts

2026-08-10

AI Summary

This work addresses the limitations of existing large language model–based multi-agent systems, which struggle with complex, long-horizon tasks due to a lack of parameter-level heterogeneity. The authors propose a task-oriented multi-agent framework that decomposes tasks into dependency-aware directed acyclic graphs and assigns each agent a (role, subtask) pair. By introducing dynamic mixture-of-LoRA experts coupled with semantic routing, the framework achieves role–subtask conditional specialization for the first time at both structural and parametric levels. To stabilize cooperative training under sparse rewards, they further design a hierarchical grouped relative policy optimization algorithm alongside a two-level credit assignment mechanism. Experiments demonstrate significant improvements in both overall and step-level performance across three backbone models on code generation benchmarks, with the learned specialization generalizing effectively to unseen tasks and domains.

Abstract

Large language model-based multi-agent systems have recently shown strong potential for complex, long-horizon tasks. However, existing methods mainly rely on coarse prompt-level differentiation without parameter adaptation for diverse subtasks, resulting in insufficient inter-agent heterogeneity and limited specialized capability that bottleneck performance on tasks with complex requirements. To address this, we introduce a Task-Oriented Multi-Agent System with Mixture of Role-Subtask Experts (MoRSE) that distinguishes agents with (role, subtask)-conditional specialization at both the task structure and parameter levels. To make agents' responsibility explicit at the task structure level, we formulate a task-oriented multi-agent system that decomposes each task into a dependency-aware Directed Acyclic Graph of subtasks and assigns each agent a specific (role, subtask), introducing task-level specialization across collaborating agents. Additionally, to address the diverse role and subtask parameter adaptation demands, we propose a dynamic Mixture of (role, subtask) LoRA Experts module with a prototype-based semantic router for subtasks, augmenting agents with parameter-level specialization on a shared LLM substrate cost-effectively. Then, to co-optimize experts and router stably under sparse task rewards, we further propose a hierarchical group-relative policy optimization with two-layer credit assignment that isolates expert updates from the cross-route variance introduced by routing decisions, disentangling expert quality from routing quality. Experiments on code-generation benchmarks across three backbones demonstrate the effectiveness of our approach, with improvements in both whole-task and step-wise performance, and the gains from trained specialization generalize across held-out task categories and domains.

Problem

• multi-agent system
• task decomposition
• parameter adaptation
• agent heterogeneity
• specialized capability

Innovation

• Mixture of Experts
• Role-Subtask Specialization
• Multi-Agent System
• LoRA
• Hierarchical Credit Assignment