RPO-MAS: RECEIVER PREFERENCE OPTIMIZATION FOR SELECTIVE MULTI-AGENT COMMUNICATION

Communication is a critical component of multi-agent reasoning, yet peer communication does not always improve the receiver's decision. A peer response that is correct in isolation may be redundant or harmful for a receiver with a different reasoning state, while autonomous refinement or preserving the current response may lead to better outcomes. Existing approaches for selective multi-agent communication mainly rely on source reliability, peer selection, or communication structures, providing limited supervision on whether a particular intervention benefits the receiver. In this work, we propose RPO-MAS, a receiver-conditioned preference optimization framework for selective multi-agent communication that learns intervention preferences from receiver-side outcomes. RPO-MAS formulates communication selection over three intervention types: peer-conditioned updating (SEND), autonomous refinement (SELF), and state preservation (KEEP). Starting from the same receiver state, it compares alternative interventions and constructs outcome-based preferences from their observed correctness outcomes. Based on these preferences, RPO-MAS learns a receiver-conditioned scorer to select appropriate interventions among candidate actions. Evaluation across seven reasoning benchmarks with Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct agents shows that RPO-MAS consistently improves multi-agent reasoning performance over independent aggregation and communication-based baselines. Compared with Majority Voting, RPO-MAS improves average accuracy from 76.91% to 77.72% and from 72.42% to 73.13% on the two backbones, respectively. Further analysis shows that receiver-conditioned supervision improves correction of erroneous states while reducing harmful changes to correct states.