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新的PAR方法提升语言模型长度泛化能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种名为Progress-Aligned Recurrence (PAR) 的新方法，以提高循环语言模型在状态跟踪任务中的长度泛化能力。PAR解决了两个关键问题：它将每次迭代的预测与迄今为止的进展对齐，并重新缩放循环状态以保持一致的尺度。这种方法使模型能够显著外推到比训练时更长的序列，并在swap-S5等任务上取得高精度。 AI

影响 使语言模型能够处理显著更长的序列，有可能提高复杂序列任务的性能。

排序理由 详细介绍语言模型新方法的学术论文。

在 Hugging Face Daily Papers 阅读 →

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新的PAR方法提升语言模型长度泛化能力

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Pan Wang, Yihao Hu, Guangyuan Dong, Yitong Lin, Hang Wang, Zimi Liu, Zhihao Wen, Jianshe Li, Jiang-Ming Yang, Wei Wu, Yongqi Tong ·

    PAR: 进度对齐的循环用于状态跟踪中的长度泛化

    arXiv:2610.27628v1 Announce Type: new Abstract: Looped language models can run more iterations at test time, but additional iterations need not help them solve longer problems. For sequential state tracking, final-answer supervision does not specify how far each iteration should …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    PAR：用于状态跟踪中长度泛化的进度对齐递归

    Progress-Aligned Recurrence trains looped language models to match intermediate states and stabilize recurrent norms, enabling strong length extrapolation on sequential tracking tasks.

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