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新的计算链架构提升了大型语言模型的规划能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为计算链（Chain of Computation, COC）的新计算架构，以提高大型语言模型（LLMs）的规划能力。该架构将基于Transformer的模型集成到一个迭代循环中，并利用一个保持恒定大小但允许选择性信息访问的结构化上下文窗口（Structured Context Window, SCW）。实验表明，即使是使用COC训练的较小模型，在诸如BlocksWorld和Pancake Puzzle等规划问题上也能达到超过99.89%的成功率，并能从最少的训练数据中进行泛化。进一步的增强，例如符号算术支持或SCW的确定性下推自动机表述，使COC能够解决数百万次操作的复杂Tower of Hanoi问题实例。 AI

影响 增强了大型语言模型在复杂规划任务上的性能，可能催生更复杂的AI代理。

排序理由 该集群包含一篇详细介绍改进大型语言模型规划能力新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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新的计算链架构提升了大型语言模型的规划能力

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该集群包含一篇详细介绍改进大型语言模型规划能力新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ehsan Futuhi, Nathan R. Sturtevant ·

    Planning with Transformers: Chain of Computation and Structured Context Windows

    arXiv:2607.17710v1 Announce Type: new Abstract: Large Language Models (LLMs) have had a remarkable impact across many areas of machine learning. However, recent studies have shown that they struggle to reliably solve planning problems. At the same time, theoretical results have s…

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