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

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

A deep dive into the challenges and solutions for efficient Reinforcement Learning (RL) in enterprise settings. The talk contrasts synchronous and asynchronous RL, explains the critical trade-off of "staleness" versus stability, and details a first-principles system model used to optimize GPU allocation for maximum throughput.

Introducing serverless reinforcement learning: Train reliable AI agents without worrying about GPUs

Introducing serverless reinforcement learning: Train reliable AI agents without worrying about GPUs

Kyle Corbett and Daniel from CoreWeave (formerly Openpipe) discuss the practical advantages of Reinforcement Learning (RL) over Supervised Fine-Tuning (SFT) for building reliable and efficient AI agents. They introduce Serverless RL, a new platform designed to eliminate the infrastructure complexities of RL training, and share a playbook for teams looking to get started.