Scaling Reinforcement Learning with torchforge on CoreWeave Cloud

This post details the integration of torchforge, a PyTorch-scalable RL framework, with CoreWeave's Slurm on Kubernetes (SUNK) offering to enable large-scale Reinforcement Learning (RL) training and post-training workflows. It highlights a collaboration with Meta and Stanford's Scaling Intelligence Lab that successfully ran a dense coding model on 512 NVIDIA H100 GPUs using torchforge on CoreWeave Cloud. The post emphasizes how torchforge separates algorithmic logic from infrastructure complexity, and how SUNK's advanced scheduling and orchestration capabilities (handling over 32,000 GPUs) provide reliability, efficiency, and faster job startup for RL workloads.