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Learn how to design and scale infrastructure for large-scale distributed reinforcement learning from Prime Intellect's engineering team in this 30-minute conference talk from Ray Summit 2025. Discover the architecture behind prime-rl, an async-first RL trainer built for massive distributed runs spanning multiple clusters with fault-tolerant execution and heterogeneous inference pools leveraging spot compute for rollout workers. Explore how prime-rl supports complex multi-turn environments through verifiers, Prime Intellect's library for building agentic protocols around OpenAI-compatible APIs that enable direct offline evaluation using any model endpoint. Understand how large RL training runs for models like INTELLECT-3 utilize the Environments Hub, a community-driven platform for sharing train-ready RL environments as importable Python modules that enables modularity, rapid experimentation, and reuse across complex training pipelines. Examine the Prime Compute platform, a multi-cloud compute marketplace supporting everything from large-scale training clusters and inference deployments to secure sandboxes required for sophisticated agentic environments. Gain insights into architecting distributed RL at scale, designing tooling for multi-turn agentic workflows, and building compute substrates that support next-generation RL-driven AI systems.
