English(EN) Efficient Long-Horizon Learning for Learned Optimization

新的ELO算法增强了学习型优化器在长时域任务上的性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-09 04:00

研究人员开发了一种新的元训练算法，称为高效长时域（ELO）学习，以解决当前学习型优化器（LOs）的局限性。ELO通过将计算重新分配到更长的失败模式，并提供解耦的渐进式专家监督来稳定学习信号，从而有效地将元训练扩展到长时域的内部问题。这种方法提高了LOs在语言建模和图像分类等下游任务上的性能和分布外泛化能力，ELO训练的优化器持续优于AdamW，并与Muon竞争。 AI

影响这种新的元训练算法可能带来更高效、更有效的AI模型优化器，从而提高在复杂任务上的性能。

排序理由该集群包含一篇详细介绍学习型优化新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Xiaolong Huang, Benjamin Th\'erien, James Harrison, Eugene Belilovsky · 2026-07-09 04:00

Efficient Long-Horizon Learning for Learned Optimization

arXiv:2607.06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.g., Adam and Muon) by meta-learning small neural network optimizers over a distribution of tasks. While recent work has greatly advanced the architectural design …

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Efficient Long-Horizon Learning for Learned Optimization

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