Learning Informative and Efficient Multi-Agent Communication Topologies via Fine-Grained Information Gain Rewards

The communication topology in large language model (LLM)-based multi-agent systems governs how information flows among agents, critically shaping both task performance and communication efficiency. However, existing topology learning methods typically rely on graph-level outcome rewards, which provide limited feedback on individual edge contributions. Consequently, even successful topologies may retain redundant or harmful interactions, introducing unnecessary communication overhead and potentially hindering collaboration. To address this limitation, we propose EdgeIG, an information-theoretically guided framework for fine-grained reward modeling in adaptive communication topology learning. Specifically, EdgeIG guides topology generation policy learning based on the information gain from each transmitted message, which is measured by how that message changes the model’s support for the ground-truth answer. The resulting edge-level rewards provide dense supervision, penalizing harmful interactions even within successful topologies. We further combine these rewards with a graph-level advantage to jointly improve task performance and communication efficiency. Extensive experiments on six benchmarks spanning general reasoning, mathematical reasoning, and code generation demonstrate that EdgeIG improves task performance and communication efficiency while maintaining strong robustness under prompt attacks.