The report analyzes GRPO, PipelineRL, and policy staleness.
RL Systems Mind the Gap: Matching Trainer and Generator Throughput RL Training Infrastructure, GRPO, PipelineRL, Async RL, Policy Staleness, RL Sandbox Infra, CPU Requirements, TCO Analysis, Thinking Machines Tinker
https://newsletter.semianalysis.com/p/rl-systems-mind-the-gap-matching
Users praise SemiAnalysis reports on RL training throughput matching for clearly explaining efficiency gains, calling the analysis refreshing and valuable.
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Great RL systems deep dive by @SemiAnalysis_
Scaling RL is as much of an infra problem as an algorithm one
SemiAnalysis ran experiments on our stack: Prime RL + Sandboxes. System efficiency is ultimately queue health to match generator and trainer throughput
RL Systems Mind the Gap: Matching Trainer and Generator Throughput RL Training Infrastructure, GRPO, PipelineRL, Async RL, Policy Staleness, RL Sandbox Infra, CPU Requirements, TCO Analysis, Thinking Machines Tinker
https://newsletter.semianalysis.com/p/rl-systems-mind-the-gap-matching
awesome post by @kimbochen covering RL systems end-to-end, including a SWE training run on GLM-5 using our prime-rl framework.
RL Systems Mind the Gap: Matching Trainer and Generator Throughput RL Training Infrastructure, GRPO, PipelineRL, Async RL, Policy Staleness, RL Sandbox Infra, CPU Requirements, TCO Analysis, Thinking Machines Tinker
https://newsletter.semianalysis.com/p/rl-systems-mind-the-gap-matching
@kimbochen also he has a cat (chaton) profile picture so you can trust him
nice blog by @kimbochen about the current RL ecosystem, goes into detail about the different settings and tradeoffs to consider when RLing open models
Our friends + occasional antagonists at @SemiAnalysis_ published a great writeup on RL training efficiency: treat the system as a queue and keep generator and trainer throughput matched.
Also includes an analysis of Tinker's cost-efficiency and many OSS RL frameworks!
RL Systems Mind the Gap: Matching Trainer and Generator Throughput RL Training Infrastructure, GRPO, PipelineRL, Async RL, Policy Staleness, RL Sandbox Infra, CPU Requirements, TCO Analysis, Thinking Machines Tinker
https://newsletter.semianalysis.com/p/rl-systems-mind-the-gap-matching
@johannes_hage Thank you Johannes! prime-rl is great!
@SemiAnalysis_ @dylan522p You can get quickly started on rl training using our hosted training
https://docs.primeintellect.ai/hosted-training/getting-started
RL training systems touch almost every software system that was designed to make language models useful: an optimization stack from pretraining, an inference engine from when language models became useful enough to serve, and APIs wrapped in sandboxes to support tool use. Orchestrating the three components together is already tricky, but there's an additional confounder of the workload getting harder and harder over time (response lengths growing, accumulating staleness, etc).
RL Systems Mind the Gap: Matching Trainer and Generator Throughput RL Training Infrastructure, GRPO, PipelineRL, Async RL, Policy Staleness, RL Sandbox Infra, CPU Requirements, TCO Analysis, Thinking Machines Tinker
https://newsletter.semianalysis.com/p/rl-systems-mind-the-gap-matching
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@tinkerapi @SemiAnalysis_ efficiency gains from matched throughput are actually rare to see broken down this clearly
refreshing read for the "just add more gpus" crowd
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