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新的蒸馏方法提升大语言模型训练效率

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了新的在线蒸馏（OPD）方法来改进语言模型的训练。其中一种方法，教师门控在线蒸馏（TGOPD），在应用监督之前，在提示级别验证教师的可靠性，在各种领域和规模上均优于普通OPD。另一种方法将离线教师优化与在线学生蒸馏相结合，显示出显著的收益，尤其是在分布变化下，并取得了与更大模型相媲美的性能。 AI

影响 这些蒸馏技术可能导致更高效地训练更小、高性能的语言模型，从而提升部署能力。

排序理由 arXiv上发表了两篇研究论文，详细介绍了语言模型训练中在线蒸馏的新方法。

在 arXiv cs.AI 阅读 →

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新的蒸馏方法提升大语言模型训练效率

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arXiv上发表了两篇研究论文，详细介绍了语言模型训练中在线蒸馏的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhiwei Zhang, Zechen Sun, Fei Zhao, Kang Peng, Bin Liang, Huayu Deng, Yao Hu, Kam-Fai Wong, Mu Chuan ·

    蒸馏前验证：基于策略的蒸馏的提示级教师门控

    arXiv:2609.02998v1 Announce Type: cross Abstract: On-policy distillation (OPD) accelerates post-training by providing dense token-level supervision from a frozen teacher on the student's own rollouts. Vanilla OPD applies this supervision uniformly across prompts, without checking…

  2. arXiv cs.AI TIER_1 English(EN) · Vignesh Prabhakar, Jialing Pan, Anil Babu Ankisettipalli ·

    On-Policy Distillation Meets Off-Policy GRPO: Training Compact Instruction-Following Rerankers

    arXiv:2609.01947v1 Announce Type: cross Abstract: Compact instruction-following rerankers are attractive for deployment, but conventional distillation pipelines typically train students by offline imitation of teacher outputs on a fixed set of examples, constraining supervision t…

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