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Explore the major milestones and engineering insights behind the release of RLlib v2 stack in this 32-minute conference talk from Ray Summit 2025. Learn about the complete redesign of RLlib's architecture for next-generation large-scale reinforcement learning workloads, focusing on higher throughput, improved reliability, and easier extensibility. Discover the key enhancements that enable RLlib to scale to 10,000+ environment runners and 100+ learners, supporting both massive distributed training and complex real-world RL pipelines. Gain insights into production learnings that shaped the new architecture and understand what's ahead for RLlib, including deeper integration with diverse simulators and expanded support for large-scale, multi-agent, and hybrid RL workloads. Understand how RLlib is evolving to meet the demands of modern AI systems, whether you're building simulation-heavy pipelines, scaling RL research, or deploying RL in production environments.
