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English(EN) On the Diverse Dynamical Behaviors Arising in Deep Linear Transformers

研究人员探索自适应深度和循环折叠以优化 Transformer

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文探索了通过动态调整深度来优化 Transformer 模型的新方法。第一篇论文《Looped Transformers 中的自适应深度》研究了学习到的停止门和轨迹读出，发现固定的先验深度监督可以带来更好的性能和实际的推理时间节省。第二篇论文《Mobius Learning: Transformers 中的循环深度折叠》介绍了一种训练架构，其中不同的数据流遵循循环移位的块顺序，允许块同时针对浅层和深层角色进行优化，并在内存受限的分布式训练方面显示出潜力。 AI

影响 这些研究论文探索了优化 Transformer 模型的新方法，可能带来更高效、更有效的 AI 系统。

排序理由 两篇 arXiv 论文介绍了优化 Transformer 架构的新方法。

在 Hugging Face Daily Papers 阅读 →

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研究人员探索自适应深度和循环折叠以优化 Transformer

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Andrei Cristian Popescu, Haitz S\'aez de Oc\'ariz Borde, Pietro Li\`o ·

    Looped Transformers 中的自适应深度：诊断学习到的暂停门和轨迹读出

    arXiv:2607.20519v1 Announce Type: new Abstract: Looped Transformers increase test-time computation by repeatedly applying a shared recurrent block. Learned halting objectives in looped Transformers typically use a single exit distribution both as the inference-time stopping rule …

  2. arXiv cs.CL TIER_1 English(EN) · Tongtian Zhu ·

    Mobius Learning：Transformer中的循环深度折叠

    arXiv:2607.17843v1 Announce Type: cross Abstract: Transformer-based language models organize computation along an ordered depth axis, where shallow and deep blocks often develop distinct representational roles. We challenge the conventional view that these roles must remain tied …

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

    深度线性Transformer中涌现的多样化动力学行为

    We study the inference-time behavior of deep linear encoder-only transformers through the lens of interacting particle systems. In this perspective, tokens are modeled as particles that interact dynamically through successive linear self-attention layers. We show that in embeddin…

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