Revisiting White-Box Communication in Multi-Agent Reinforcement Learning

Communication is essential for coordinating the behaviors of multiple agents. However, existing black-box communication methods often rely on complex optimization mechanisms and produce communication behaviors that are difficult to interpret. Recent advances in large language models (LLMs) substantially reduce the effort required to construct white-box communication protocols, raising a natural question: can white-box communication become a competitive alternative to learned communication? In this work, we systematically revisit white-box communication by studying its effectiveness, sources of effectiveness, capability boundaries, and potential value for learned communication. We first show that LLM-generated white-box protocols achieve performance competitive with learned communication methods across decisions about what, when, and whom to communicate. Further analysis identifies advances in foundation-model capabilities as a key source of this effectiveness. We then characterize the capability boundaries of white-box communication. While scaling the number of agents alone does not necessarily degrade performance, increasing agent heterogeneity and protocol complexity poses greater challenges; performance also declines as coordination-relevant semantics become less explicit, and feedback-driven self-refinement remains unstable. These findings reveal a natural complementarity between white-box and learned communication: white-box protocols provide explicit communication knowledge but are difficult to refine reliably, whereas learned policies can continue improving through RL. We therefore use white-box protocols as teachers for neural communication policies, improving learning efficiency while retaining the capacity for further optimization. Taken together, our study demonstrates that white-box communication deserves renewed attention as a viable direction for multi-agent communication research.