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Explore the major milestones and architectural improvements behind RLlib v2 in this 32-minute conference talk from Ray Summit 2025. Learn from Anyscale engineers Artur Niederfahrenhorst and Simon Lars Zehnder as they detail the key lessons that shaped the redesigned RLlib v2 stack, now generally available and built for next-generation large-scale reinforcement learning workloads. Discover the engineering enhancements that significantly boost scalability, reliability, and extensibility, enabling RLlib to scale to over 10,000 environment runners and 100+ learners for massive distributed RL training with high throughput. Gain insights into the future roadmap for RLlib, including planned deeper integrations with various simulators and expanded capabilities for complex, simulation-heavy RL applications. Whether you're developing large-scale RL systems, accelerating research pipelines, or deploying reinforcement learning solutions in production environments, understand how RLlib is evolving to meet modern AI infrastructure demands and requirements.
