End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning

Large language models (LLMs) are versatile, yet their deployment in complex real-world settings is limited by static knowledge cutoffs and the difficulty of producing controllable behavior within a single inference. Multi-agent search systems (MASS), which coordinate specialized LLM agents equipped with search tools, mitigate these issues via task decomposition and retrieval-augmented problem solving. However, optimizing LLMs for agent-specific roles remains labor-intensive with prompt engineering or supervised fine-tuning, motivating automated end-to-end training. Existing multi-agent reinforcement learning (MARL) methods such as Multi-Agent Proximal Policy Optimization (MAPPO) typically depend on large critic networks to evaluate joint actions, leading to instability and high memory costs. We introduce Multi-Agent Heterogeneous Group Policy Optimization (MHGPO), which updates policies by estimating relative advantages across heterogeneous groups of multi-agent rollouts, shifting the optimization focus from local agent performance to global system success. We further study three group rollout sampling strategies to trade off sample efficiency and optimization quality. Experiments show that MHGPO captures implicit inter-agent dependencies and consistently outperforms strong baselines in both task performance and computational efficiency.