English(EN) STABLE: A Continual Learning Optimizer with Adaptive Drift Control

新的STABLE优化器解决了持续学习中的灾难性遗忘问题

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

研究人员开发了STABLE，一种新的对偶优化器，旨在解决持续学习中的灾难性遗忘问题。该优化器自适应地控制参数漂移，在不牺牲可塑性的情况下确保稳定性。实验表明，STABLE与PEFT基线结合使用时，在单模态和多模态任务上均能达到或超过现有的专业持续学习方法的性能，甚至在后者中保留了零样本迁移能力。 AI

影响该优化器可以提高人工智能系统在不忘记先前知识的情况下学习新信息的能力，这对于长期适应至关重要。

排序理由该集群描述了一篇详细介绍持续学习新优化器的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群描述了一篇详细介绍持续学习新优化器的新研究论文。[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) · Xiaoyan Li, Lanpei Li, Massimo Coppola, Vincenzo Lomonaco, Antonio Carta · 2026-11-11 05:00

STABLE：一种具有自适应漂移控制的持续学习优化器

arXiv:2611.10655v2 Announce Type: replace-cross Abstract: Continual learning suffers from catastrophic forgetting, leading to the loss of past knowledge and unstable adaptation across task streams. Existing approaches typically address this issue through replay buffers, architect…

本文如何被排名

报道来源 [1]

STABLE：一种具有自适应漂移控制的持续学习优化器

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