Scaling reinforcement learning at Applied Compute | Modal Blog

What this post added

This post details how Applied Compute leverages Modal's platform for Reinforcement Learning (RL) training. It highlights the use of Modal Sandboxes for creating complex, high-fidelity training environments, Modal Functions for massively parallel CPU computation in the grading layer, and Modal's fast container startup and caching for efficient GPU utilization during rollouts. The post emphasizes Modal's ability to provide distinct infrastructure profiles for each RL loop component (rollouts, evals, inference) while maintaining low-cost boundaries between them.