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Scratchpad Patching 改进了字节级语言模型

研究人员推出了一种用于字节级语言模型的新技术——Scratchpad Patching (SP),该技术将计算需求与块大小解耦。该方法通过在块内插入临时性暂存器来解决更大块(减少计算和 KV 缓存)与建模质量之间的权衡问题。这些暂存器会刷新后续预测的上下文,与传统的字节级基线相比,提高了模型质量,并显著减少了推理计算和 KV 缓存占用。 AI

影响 这项技术可能带来更高效、更高质量的字节级语言模型,降低计算成本和内存使用量。

排序理由 这是一篇描述语言模型新技术的 ist 研究论文。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Scratchpad Patching 改进了字节级语言模型

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这是一篇描述语言模型新技术的 ist 研究论文。 [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Lin Zheng, Vasilisa Bashlovkina, Timothy Dozat, Dan Garrette, Laura Rimell, Joshua Maynez ·

    Scratchpad Patching:在字节级语言模型中解耦计算与Patch大小

    arXiv:2605.09630v2 Announce Type: replace Abstract: Tokenizer-free language models eliminate the tokenizer step of the language modeling pipeline by operating directly on bytes; patch-based variants further aggregate contiguous byte spans into patches for efficiency. However, the…