POPA ReconRL Systems Mind the Gap: Matching Trainer and Generator Throughput
RL Systems Mind the Gap: Matching Trainer and Generator Throughput Reinforcement learning (RL) training system efficiency dictates how far model capabilities can scale, and this efficiency ultimately relies on successfully matching the throughput of the generator and the trainer to prevent infrastructure starvation or excessive policy staleness Modern RL runs are heavily generation-bound due to long model response traces, intensive tool-use verification, and sandbox scaling bottlenecks that leave the trainer idling for a significant percentage of compute time https://newsletter.semianalysis…
The excerpt is quoted from a public Telegram channel, which owns its text.