Norsk(NO) Breaking the Token Ceiling: Distilling Smaller, Stronger Byte Models

大规模研究表明，字节模型比Token模型展现出更高的性能上限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员探讨了基于Token和基于字节的语言模型在性能上的差异，特别是在蒸馏的背景下。他们提出了两种方法，Marginalize-It和End-Of-Token，用于将Token logits转换为字节logits。他们的大规模研究发现，虽然在较低的计算水平下，Token模型最初的表现优于字节模型，但随着计算量的增加，字节模型最终会超越它们，达到更高的性能上限。蒸馏后的字节模型还表现出更高的能效和更低的存储成本。 AI

影响基于字节的模型可能为未来语言模型的发展提供更具可扩展性和效率的路径。

排序理由该集群包含一篇学术论文，详细介绍了关于语言模型架构和性能的新研究。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 Norsk(NO) · Kalyani Marathe, Artidoro Pagnoni, Tomasz Limisiewicz, Margaret Li, Mike Lewis, Luke Zettlemoyer, Srinivasan Iyer · 2026-09-14 04:00

打破Token天花板：提炼更小、更强的Byte模型

arXiv:2609.12303v1 Announce Type: new Abstract: Small models are made more capable through distillation from a larger one that shares their tokenization scheme. However, do distilled byte and token models behave similarly in terms of scaling trends as compute and data increases? …

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打破Token天花板：提炼更小、更强的Byte模型

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