How Open Frontier Labs Actually Train Their Models

[2026 - Day 3 - Model Systems] Training a large language model is an exercise in tradeoffs you didn’t expect, where choices made for pre-training can shape post-training, agentic RL, and inference much later. Should you spend a week optimizing infrastructure and architecture, or just start training when every design choice affects rollout speed, memory use, and serving cost? This talk covers how to think about mdeol decisions: why architecture changes are rarely just about accuracy and almost always about performance, how inference choices determine what is feasible for post-training, and how frontier open labs design models for RL-heavy, agentic workloads. We’ll walk through the full lifecycle of a model, from pre-training to mid-training to post-training RL, examining how decisions at each stage shape the next and why tradeoffs around efficiency, capability, and inference cost rarely have clean answers SPEAKER: Sami Jaghouar - Head of Research, Prime Intellect 👉 Sign up for our "No BS" Newsletter to get the latest technical data & AI content: https://aicouncil.com/newsletter ABOUT AI COUNCIL: AI Council brings together the brightest minds in data to share industry knowledge, technical architectures and best practices in building cutting edge data & AI systems and tools. FIND US: Website: https://aicouncil.com/ LinkedIn:   / aicouncilconf   X: https://x.com/aicouncilconf