Scaling Reinforcement Learning on Modal

Scaling reinforcement learning (RL) infrastructure on Modal relies on leveraging core primitives like functions, volumes, and sandboxes to optimize stability and throughput. Training efficiency improves by utilizing multi-node clusters and parallelizing workloads, while maintaining warm pools of sandboxes ensures rapid environment creation for rollouts. Integrating frameworks like SLIM and Harbor enables complex workflows, such as using LLMs as judges for reward computation or solving CodeGolf puzzles through automated sandbox execution. These design patterns allow developers to transition from single-node experiments to large-scale parameter sweeps and multi-node training jobs. By treating infrastructure as a programmable platform rather than a static solution, developers achieve faster training loops and more reliable evaluation metrics for coding agents and other RL-driven applications.