Training Agents IRL
Register with organizerWhat to look forward to
GitHub and Hugging Face will bring together ML practitioners and RL researchers for an evening diving into how AI agents are trained and evaluated—covering everything from reward design and multi-step training to the real-world gaps that matter. Expect rapid-fire talks from industry builders at Microsoft, Hugging Face, and emerging agent startups, plus a panel unpacking what actually works in agent RL and where the ecosystem needs to go next.
Inside the event
An open-source AI community event on reinforcement learning for agentic systems, hosted by GitHub and Hugging Face. Join the open-source community at GitHub in San Francisco for an evening with researchers and builders exploring how AI agents are trained and evaluated. We will look beyond single-turn RL to multi-step, environment-driven training: reward design, rollouts, benchmarks, and the practical gap between training gains and real-world agent behavior. What to expect Short, 10-minute talks with Q&A, followed by a closing panel on what works in RL for agents, where the bottlenecks are, and what the open-source ecosystem needs next. Speakers: - Ben Burtenshaw (HF) - Govind Kamtamneni (Microsoft) - Victor Barres (Mercor) - Zach Wentz (Reflection) - Tao Lin (RadixArk) - Xiangyi Li (Benchflow) Who should attend ML practitioners, agent builders, and RL researchers. Thursday, October 22, 2026 · 6–9pm Pacific GitHub, San Francisco
This is part of Open Source AI Week.
Meet in San Francisco, CA
City view. The source has not supplied a venue; check the organizer for the exact location.
Open in Google Maps ↗Discovered through Cerebral Valley. Details come from the event listing; registration is managed by the organizer.
Last checked: Oct 13, 2026. Confirm current availability before making plans.