Scaling reinforcement learning at Applied Compute | Modal Blog

This post details how Applied Compute uses Modal's sandboxes to create complex, production-mimicking environments for RL training, emphasizing fidelity and determinism. It highlights Modal's performance optimizations, specifically fast container image caching and sub-second cold starts, which keep GPUs utilized by minimizing CPU-bound idle time during rollouts. The post also explains the use of Modal Functions for massively parallel grading, leveraging automatic retries and per-invocation isolation for reliability under load. This contributes to the AI Research Infrastructure capability by demonstrating its application in a complex RL training loop.