Reinforcement learning is an infrastructure problem

This article argues that the primary challenges in deploying reinforcement learning systems are infrastructure-related rather than algorithmic, focusing on issues like distributed training, simulation environments, and resource management. It likely discusses how proper infrastructure design is essential for making reinforcement learning practical at scale and why many RL projects fail due to inadequate engineering foundations rather than flawed ML approaches.

Source: Modal Blog | 2026-06-01