SocialMAD: Social Learning-Based Multi-Agent Debate

Multi-Agent Debate (MAD) uses diverse independent judgments to provide complementary perspectives and correct reasoning errors. However, more interaction does not necessarily improve collaboration: group information can resolve errors but also interfere with agents that are already correct. Existing methods typically share all raw responses and use fixed rules such as conformity, which can interfere with valuable judgments and lead to erroneous consensus. Thus, the key to MAD is not more interaction, but enabling agents to learn how to use the resulting group information and turn diverse judgments into effective reasoning gains. From a social learning perspective, we propose SocialMAD, which reformulates collaboration in MAD as the optimization of personalized social learning strategies. SocialMAD organizes mixed raw responses into structured social information, encodes behaviors such as conformity and self-persistence as composable strategy units, and learns agent-specific social learning strategies from limited training data. These strategies guide agents to adaptively use group information during collaborative reasoning without predefined uniform rules. Experiments show that SocialMAD improves accuracy by up to 10.00% over the strongest baseline and increases the error-correction rate by approximately 19.38%. The learned strategies transfer to unseen tasks and benefit from more agents and interaction rounds.