This novel approach to language model fine-tuning introduces a multiagent framework that leverages a team of specialized models with distinct roles, promoting diversity in reasoning and sustaining long-term performance gains. By employing a society of agents with varied responsibilities, such as generation and criticism, the system iteratively improves itself through autonomous self-improvement, achieving significant performance gains across various reasoning tasks. This method has been successfully tested on both open-source and proprietary models, demonstrating its versatility and broad applicability. The framework maintains response diversity by ensuring each agent is trained only on its own correct responses, mitigating the collapse into uniform outputs often seen in single-agent fine-tuning. However, challenges such as maintaining diversity, coordinating individual performance with system effectiveness, and optimizing computational resources remain to be addressed.