Agentic RL introduces new systems challenges beyond traditional single-turn RL. At PyTorch Conference North America, Yichuan Wang and Shuhua Yu will discuss how to build an end-to-end agentic RL training loop in PyTorch, covering rollout infrastructure, trainer–serving interaction, environment abstractions, sandbox execution, scheduling strategies, and the tradeoffs between on-policy and off-policy training. They will examine key design choices, practical engineering considerations, and emerging techniques and recipes for scaling multi-turn agent training, drawing lessons from recent open-source and industry systems.
Register for PyTorchCon North America today: https://hubs.la/Q04v4SL60