13% Policy Drift Staleness Suggests RL Envs Needed No Curriculum, Tokenbender Argues
tokenbender · x · 2026-09-22
tokenbender reads a new RL report: large throughput, heterogeneous environments, and only 13% policy drift staleness suggest the chosen tasks were runnable or improvable directly on base weights, with no curriculum learning required.
The argument: tasks demanding skills learned across earlier RL updates would have suffered staleness penalties. The author poses an open question—how would results change with a harder task set?
Related event: Xiaomi MiMo's 1M-Context RL Training Details Spark Debate(13 posts)→
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