Rectified Looped Transformer 提高了稳定性和效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-14 05:00

研究人员推出了一种新颖的架构——Rectified Looped Transformer (RLT)，旨在提高循环 Transformer 的效率和稳定性。与可能出现状态尺度增长和扰动放大的标准循环 Transformer 不同，RLT 采用了一种受流模型启发的插值方案。该方法在线性地插值循环的当前状态与其预测之间，分配状态变化并促进更平滑的轨迹。理论分析表明 RLT 可以限制状态幅度并减少扰动放大，而经验评估显示，在没有额外参数或计算成本的情况下，它实现了更低的困惑度和更高的鲁棒性。 AI

影响为深度 Transformer 架构引入了一种更稳定且参数效率更高的方法，有可能提高语言建模任务的性能。

排序理由该集群描述了一篇详细介绍新型 Transformer 架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Musheng Li, Jiawei Zhang, Ziyuan Liu, Yuantao Gu · 2026-11-14 05:00

Rectified Looped Transformer

arXiv:2611.16265v1 Announce Type: cross Abstract: Looped Transformers increase effective depth by repeatedly applying a shared block, offering a parameter-efficient approach to language modeling. However, most existing works directly reuse each loop's output as the next's input, …

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报道来源 [1]

Rectified Looped Transformer

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