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Uniform token-level distillation often punishes valid student reasoning, but dynamically reallocating teacher supervision using verified outcome agreement unlocks immediate gains across math and code tasks without relying on hard trajectory filtering.
Stale rollouts can introduce a significant bias in learning rates, fundamentally altering the stability landscape of asynchronous RLHF systems.