Optimizing Infrastructure Crucial for Coding-Agent RL Efficiency.

Coding-agent reinforcement learning (RL) heavily relies on numerous interactive software rollouts, yet the underlying execution infrastructure is often treated as a secondary implementation detail. This research highlights a missed opportunity for efficiency improvements by measuring the infrastructure overhead associated with these rollouts. Small per-rollout savings can compound significantly at scale, especially during RL post-training phases. The study conducted a comparative analysis of four execution substrates: single containers, hosted sandboxes, Kubernetes-orchestrated containers, and cloud virtual machines. Findings revealed a stark contrast, with up to 110 times variation in cold-start latency and a 1.8 times difference in projected worker-hours for one million 150-step trajectories. The results strongly suggest that future coding-agent RL systems should integrate execution substrate optimization directly into the training system design, rather than viewing it merely as a deployment concern.