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Two observations trace this gap. First, greedy \\textsc{pass}@$1$ nearly vanishes after compression, yet \\textsc{pass}@$k$ recovers substantially under repeated sampling: useful generations are demoted, not erased. Second, the recoverable regime fails mainly through suffix repetition. Recovery should therefore train on the compressed model's own on-policy states with dense token-level supervision, which On-Policy Distillation (OPD) provides by reusing the pre-compression model as a frozen teacher. However, long on-policy rollouts spend early recovery budget on low-information repetitive suffixes, delaying loss descent. To mitigate this waste, we propose \\textbf{\\shortopd}, a short-to-long OPD sched","title":"ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation","url":"https://arxiv.org/abs/2607.13124","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.13124v1 Announce Type: cross \nAbstract: Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compressed checkpoints can collapse on the free-form generation that deployment actually requires. Two observations trace this gap. First, greedy \\textsc{pass}@$1$ nearly vanishes after compression, yet \\textsc{pass}@$k$ recovers substantially under repeated sampling: useful generations are demoted, not erased. Second, the recoverable regime fails mainly through suffix repetition. Recovery should therefore train on the compressed model's own on-policy states with dense token-level supervision, which On-Policy Distillation (OPD) provides by reusing the pre-compression model as a frozen teacher. However, long on-policy rollouts spend early recovery budget on low-information repetitive suffixes, delaying loss descent. 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