English(EN) Extreme Compression of Per-Layer Embeddings via Cross-Layer Latent Vector Quantization

新的OPTIC方法实现了LLM嵌入的极端压缩

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一种名为OPTIC（Output-Path-fitted, Token-shared Indexed Codes）的新方法，用于对大型语言模型中的逐层嵌入（PLE）进行极端压缩。该技术实现了显著的压缩率，将Gemma 4 E4B的参数数量减少了约100倍，同时在多个基准测试中保留了97%以上的原始性能。OPTIC在推理过程中也显示出优势，将GPU内存使用量减少了近30%，同时对速度的影响最小。 AI

影响这项研究通过大幅减小大型语言模型的内存占用，有可能实现更高效的部署和可访问性。

排序理由详细介绍模型压缩新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 4 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍模型压缩新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jiacheng Liu, Yaxin Luo, Xiaohan Zhao, Peng Sun, Xinyi Shang, Jiacheng Cui, Yonina C. Eldar, Zhiqiang Shen · 2026-10-23 04:00

Extreme Compression of Per-Layer Embeddings via Cross-Layer Latent Vector Quantization

arXiv:2610.31309v1 Announce Type: new Abstract: Scaling embedding capacity alongside Transformer computation is an emerging trend in language models. This trend makes high compression rates increasingly useful. Hence, we study extreme compression of Per-Layer Embeddings (PLE), wh…

本文如何被排名

报道来源 [1]

Extreme Compression of Per-Layer Embeddings via Cross-Layer Latent Vector Quantization

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