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This paper introduces the length-scaling tax (LST)—excess response length on already-solved queries after RL post-training—and proposes Length Self-Distillation (LSD), which routes solved prompts to on-policy distillation using an EMA of the online policy as teacher, substantially curbing verbosity while preserving or improving accuracy.