SGS task sampling keeps robot RL improving as environments scale to a million parallel sims
ChongZzZhang · x · 2026-10-14
In a thread on how pure RL discovers robot behaviors, octizhang explains SGS: with uniform task sampling, adding parallel environments often doesn't help, as most experience goes to tasks already mastered or far out of reach. SGS tracks the success rate of every task configuration and resets more often to ones the policy is just starting to solve, keeping RL improving at scale up to a million parallel environments.
Practitioner ChongZzZang adds that while some call the idea trivial, anyone who has tried adaptive curriculum methods (monotonic success-rate scheduling, progress-based ones) will be amazed how much more stable SGS is, given its weight curve.
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