Training Agents IRL
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
Speaker lineup and detailed agenda to follow.