Reinforcement learning is an infrastructure problem | Modal Blog

What this post added

This post details the infrastructure challenges and solutions for Reinforcement Learning (RL) post-training of LLMs. It highlights the three core infrastructure problems: training at scale (multi-node, RDMA, delta compression), efficient rollouts from high-performance inference engines, and managing isolated environments for concurrent actions. Modal's platform is presented as a solution that abstracts away the complexity of maintaining glue code, queuing for cluster time, and under-utilizing GPUs. Specific features like `modal.experimental.clustered` with `rdma=True` for multi-node training, fast container boot times, and scalable Sandboxes for concurrent rollouts are discussed. The post also emphasizes the importance of open-source RL frameworks and Modal's contributions to them, such as delta compression for slime.