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REVO: Rollout-Efficient Off-Policy Distillation via Variance-Guided Reuse
On-policy distillation (OPD) trains language models using dense token-level teacher supervision on student-generated trajectories. However, its reliance on frequently refreshed student rollouts often incurs substantial generation cost. We introduce REVO, an off-policy distillation framework that imp
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