English(EN) Force without transmission: a depth-induced rank collapse that no loss on the representation reopens

Transformer秩崩溃与梯度路径相关，而非损失强度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员在Transformer中发现了一种称为“秩崩溃”的现象，其中所有token表示都对齐到单一方向，导致学习停止。实验表明，削弱跳跃连接并添加损失项并不能修复这种崩溃。问题源于梯度路径，而非损失的强度，因为梯度未能到达关键的查询和键权重。然而，恢复跳跃连接会立即重新打开路径并允许秩恢复。 AI

影响识别出Transformer中的一种关键故障模式，可能指导未来的架构改进和训练方法。

排序理由该集群包含一篇详细介绍Transformer模型特定技术发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Martin Hofmann, Patrick M\"ader · 2026-10-08 04:00

无传动力的力：深度诱导的秩崩溃，表示上无损的重新开启

arXiv:2610.09958v1 Announce Type: new Abstract: Training can drive a transformer into a rank collapse: all token representations point in one direction, and learning stops. In a related collapse of attention, a loss term with a bounded corrective force repairs the network during …

本文如何被排名

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无传动力的力：深度诱导的秩崩溃，表示上无损的重新开启

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