Reinforcement learning has moved beyond the post-training correction phase. Researchers now integrate online RL directly into agentic loops, reasoning pipelines, and iterative refinement systems where a language model serves as both policy and world model. The result is a tight feedback loop: the LLM generates a trajectory, a reward model scores the output, and the policy updates. The bottleneck is rarely the optimization math. It is the inference layer that must serve thousands of rollout requests with variable, often massive, context lengths.
The RL-LLM Inference Loop
Modern RL-enhanced LLM systems typically follow a repeated generate-then-score pattern. During each training step, the policy model produces a batch of completions. These completions are evaluated by a reward function, which may be a learned model, a hard-coded rule engine, or a call to a larger judge model. The gradients flow back through the policy, but the forward pass depends entirely on low-latency, high-throughput inference. Any friction
