English(EN) A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone

新的低秩克隆方法以 1000 倍的效率训练更小的模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种名为低秩克隆（LRC）的新方法，以创建能够模仿更大模型性能的更小、更高效的语言模型。LRC 使用投影矩阵来压缩教师模型的权重并对齐学生的激活，包括来自前馈网络的激活。实验表明，与在万亿个 Token 上训练的模型相比，LRC 使用仅 200 亿个 Token 即可实现超过 1000 倍的训练效率。 AI

影响这种方法可以显著降低训练高性能小型语言模型所需的成本和时间。

排序理由该集群描述了 arXiv 论文中提出的一种用于语言模型高效知识蒸馏的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jitai Hao, Qiang Huang, Hao Liu, Xinyan Xiao, Zhaochun Ren, Jun Yu · 2026-09-02 04:00

一个 Token 价值超过 1000 个 Token：通过低秩克隆实现高效知识蒸馏

arXiv:2505.12781v5 Announce Type: replace-cross Abstract: Training high-performing Small Language Models (SLMs) remains costly, even with knowledge distillation and pruning from larger teacher models. Existing work often faces three key challenges: (1) information loss from hard …

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一个 Token 价值超过 1000 个 Token：通过低秩克隆实现高效知识蒸馏

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