Modern RL systems balance a few core problems — how off-policy the data is, training-inference mismatch, and throughput — using asynchronous RL with separate learner and actor GPUs.

Most of modern RL is a systems problem balancing how off-policy the data is, training-inference mismatch, and throughput, built on asynchronous RL with separate GPUs for learners and actors, a design that has stayed similar for years. ✦ AI generated

Nathan Lambert · Interconnects · 2026-08-10 · original ↗

Most of modern RL is a systems problem balancing a few problems — how off-policy the data is, training-inference mismatch, and throughput. The core systems design, asynchronous RL with separate GPUs for the learners (the GPUs which take gradient steps) and actors (the GPUs which generate the rollouts in the environment), has been similar for a few years.

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