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English(EN) Taming Curvature: Architecture Warm-Up for Stable Transformer Training

新的“架构预热”稳定了Transformer训练

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新方法来稳定大型Transformer模型的训练，这类模型通常容易出现不稳定性或发散。该方法称为“架构预热”，通过逐步增加网络深度来管理预条件Hessian，这是与训练不稳定性相关的曲率度量。该技术得到了一个用于Hessian特征值快速在线估计器的支持，已被证明可以在不影响收敛的情况下减少不稳定性。 AI

影响 提高了大规模Transformer模型训练的效率和可靠性。

排序理由 该集群包含一篇研究论文，详细介绍了一种提高AI模型训练稳定性 novel 的新方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Sameera Ramasinghe, Ajanthan Thalaiyasingam, Hadi Mohaghegh Dolatabadi, Chamin Hewa Koneputugodage, Gil Avraham, Violetta Shevchenko, Yan Zuo, Karol Pajak, Alexander Long ·

    Taming Curvature: Architecture Warm-Up for Stable Transformer Training

    arXiv:2606.16768v1 Announce Type: new Abstract: Training billion-parameter Transformers is often brittle, with transient loss spikes and divergence that waste compute. Even though the recently developed Edge of Stability (EoS) theory provides a powerful tool to understand and con…

  2. arXiv cs.LG TIER_1 English(EN) · Alexander Long ·

    Taming Curvature: Architecture Warm-Up for Stable Transformer Training

    Training billion-parameter Transformers is often brittle, with transient loss spikes and divergence that waste compute. Even though the recently developed Edge of Stability (EoS) theory provides a powerful tool to understand and control the stability of optimization methods via t…

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