What problem does RL Training Infrastructure solve?

It makes online trajectory generation and policy updates throughput-efficient, observable, and recoverable.

What is the one-line intuition for RL Training Infrastructure?

Post-training is a distributed data-production system: policies generate trajectories while learners update weights under bounded staleness.

Policy/reference/reward/value state, versioned rollouts, advantages, optimizer state, and checkpoints.

Compare actor token production with learner consumption and bound policy-version staleness.

The runtime determines accelerator utilization, update freshness, recovery semantics, and training cost.

More sampled responses increase generation cost and learner input; more actors increase synchronization pressure.

Generation often dominates wall time, while large updates or weight publication can become synchronization bottlenecks.

A restarted learner may consume duplicated or stale trajectories without versioned cursors.

Track rollout tokens/s, learner tokens/s, backlog, version lag, KL, reward distribution, and checkpoint recovery time.

Separating actors from learners improves specialization but adds synchronization and placement complexity.

First quantify the bottleneck with a small ledger, then choose the method that attacks that resource directly.

PPO commonly includes value estimation; GRPO derives relative advantages within sampled groups for its stated objective.

Do not describe framework support, benchmark ranking, or frontier model behavior as current without checking official docs or papers.

A clear starting point for learning AI infrastructure.

Map

InfraLens

A clear starting point for learning AI infrastructure.

Updated May 2026.

Designed, written, and maintained by Wenbo Ji

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