Reinforcement learning infrastructure connects several systems that are often studied separately: model training, inference, data generation, reward computation, scheduling, and distributed execution.

A useful learning order is:

  1. Understand the algorithmic loop: rollout, reward, advantage estimation, and policy update.
  2. Map each stage to its system requirements.
  3. Identify where models and data move between training and inference engines.
  4. Study synchronization, staleness, batching, and resource allocation.
  5. Measure end-to-end throughput instead of optimizing one stage in isolation.

The most important systems question is usually not “Which component is slow?” It is:

Which bottleneck currently limits the rate at which useful training samples become policy updates?

That framing makes it easier to reason about asynchronous execution, reward-model capacity, rollout scheduling, and GPU allocation.