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SingularClip addresses the critical issue of plasticity loss in neural networks by clipping weight matrix singular values to counteract growing anisotropy during training. This method demonstrates robust performance across continual supervised learning and deep reinforcement learning tasks, offering a theoretical and empirical solution for maintaining adaptability in nonstationary environments. The work advances fundamental machine learning theory with direct implications for building more resilient AI systems capable of continuous operation.
SingularClip: Preventing Spectral Collapse to Maintain Plasticity in Continual and Reinforcement Learning
arXiv:2608.18319v1 Announce Type: new Abstract: Neural networks trained on nonstationary tasks frequently lose the ability to fit new targets, a phenomenon referred to as loss of plasticity. We identify a novel source of plasticity loss due to the growing anisotropy of weight matrices' singular values during training, and analyze this phenomenon both empirically and theoretically. To mitigate this issue, we introduce SingularClip, a procedure that periodically clips the singular values of all weight matrices. We show that SingularClip performs strongly against baselines across a range of tasks in both continual supervised learning and deep reinforcement learning.
Abstract:Neural networks trained on nonstationary tasks frequently lose the ability to fit new targets, a phenomenon referred to as loss of plasticity. We identify a novel source of plasticity loss due to the growing anisotropy of weight matrices' singular values during training, and analyze this phenomenon both empirically and theoretically. To mitigate this issue, we introduce SingularClip, a procedure that periodically clips the singular values of all weight matrices. We show that SingularClip performs strongly against baselines across a range of tasks in both continual supervised learning and deep reinforcement learning.
Machine Learning (cs.LG)
arXiv:2608.18319 [cs.LG]
(or arXiv:2608.18319v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.18319 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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