Meta AI Researchers Introduce Matrix: A Ray Native a Decentralized Framework for Multi Agent Synthetic Data Generation
Meta AI researchers have developed Matrix, a decentralized framework designed to enhance the generation of synthetic data for large language models (LLMs) by leveraging peer-to-peer agent scheduling on a Ray cluster. Unlike traditional centralized control systems that bottleneck scalability and GPU utilization, Matrix serializes control and data flow into message objects called orchestrators, enabling more efficient and diverse synthetic conversations while achieving 2 to 15 times higher token throughput on real workloads. This approach addresses the limitations of existing systems by distributing control logic across multiple agents, reducing coordination overhead, and significantly improving scalability for synthetic data generation. By replacing centralized controllers
