Video by Hugging Face via YouTube
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.
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