Efficient Reinforcement Learning – Rhythm Garg & Linden Li, Applied Compute

Rhythm Garg and Linden Li from Applied Compute discuss efficient reinforcement learning (RL) for enterprises, contrasting their approach with that of research labs. They highlight the importance of speed, cost-effectiveness, and reliable training time estimates. They explain the inefficiencies of synchronous RL due to idle GPUs and introduce asynchronous Pipeline RL as a solution, which allows training during sampling. They address the trade-off between staleness and learning stability, focusing on optimizing RL throughput within fixed constraints. Linden Li details a modeling approach to simulate and optimize GPU allocation between training and sampling, aiming to maximize throughput while managing staleness, and discusses the constraints and invariants necessary for an optimal asynchronous setup, emphasizing the simulation's utility in predicting performance and informing system design decisions.