Agent sandboxes multiplexed by suspending actors for RL

The author explores the practical mechanics and limitations of multiplexing reinforcement learning agent sandboxes by suspending execution during idle model generation waits. The guide covers architectural patterns, container resource sizing, and key failure modes such as model queue saturation and ghost slots. The author concludes with a structured checklist for deciding when multiplexing is appropriate and how to measure performance against a one-pod-per-rollout approach. Open article →