Daytona CEO Ivan Burazin discusses the company's pivot from developer environments to composable computers for AI agents. He explains the unique infrastructure challenges posed by spiky RL and eval workloads, Daytona's bare-metal architecture with a custom scheduler for high performance, and the future need for stateful Windows and macOS sandboxes to automate knowledge work.
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.
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.