Reinforcement learning is an infrastructure problem | Modal Blog

This post details Modal's infrastructure support for Reinforcement Learning (RL) post-training of LLMs. It highlights the challenges in training, rollouts, and isolated environment execution. Key contributions include enabling multi-node training with RDMA, improving weight synchronization with delta compression, and optimizing sandbox buffer sizing for GPU utilization. The post also emphasizes the integration with open-source RL frameworks and Modal's ability to manage compute capacity efficiently, allowing users to go from zero to a B200 cluster within minutes.