Matrix - Reliable Framework for Data-Centric Experimentation at Scale

Discover how Meta's Fundamental AI Research (FAIR) team built Matrix, a powerful framework that enables researchers to conduct large-scale, data-centric experimentation with cutting-edge LLMs and multimodal models. Learn from Hongpeng Guo, Shang-Wen Li, Ramya Raghavendra, and Dong Wang as they present their 25-minute conference talk from Ray Summit 2025, addressing the critical challenge of bridging the gap between expert-optimized tooling and the needs of research teams requiring rapid iteration capabilities. Explore Matrix's core capabilities including auto-scaled data generation from LLMs, game engines, and physics/world-model simulators triggered with single commands, easy-to-use large-scale data processing and augmentation featuring batch LLM-as-a-judge evaluations, safe sandboxed code execution, deduplication, clustering, and classification, plus reproducible evaluation pipelines designed for collaboration across large research teams. Understand how Matrix integrates with industry-standard technologies such as Ray and vLLM to enable scalable distributed compute and high-throughput inference within the framework. See how Matrix is already empowering both research and production initiatives at Meta across AGI, multimodal LLMs (MLLMs), and world modeling, while learning about its architecture, ecosystem synergy, and the high-impact research workflows it unlocks. Follow along with a practical tutorial to help you get started and contribute to the framework, gaining insights into how Matrix accelerates data-centric AI research, simplifies large-scale experimentation, and supports next-generation model development at Meta.