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This paper proposes Shadow Mask Distillation (SMD) to solve the off-policy bias caused by KV cache compression during reinforcement learning post-training for large language models. It introduces a mechanism that ensures on-policy alignment and improves memory efficiency for long-context reasoning tasks.