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Continual Learning remains one of the most challenging “holy grails” of AI.
Most discussions focus on catastrophic forgetting: models lose what they previously learned. But there is another equally important failure mode: over long continual training, neural networks can also lose their plasticity, ie, their ability to learn new things is weakened over time.
In our ICLR 2026 work with colleagues at @Apple and @ETH, we study this phenomenon, known as Loss of Plasticity (LoP), from a geometric perspective.
We show that LoP can arise when gradient dynamics become trapped in invariant manifolds of parameter space. In particular, we analyze two types of traps:
🔴 Frozen units: units saturate, gradients vanish, and they become effectively silent to backpropagation.
🔵 Cloned units: units become redundant, receive matching forward and backward signals, and move together.
For these structures, the gradient is tangent to the trap. Once standard GD/SGD enters these affine subspaces, it cannot leave them on its own. This means the dynamics can remain sticky even when the data distribution or task changes.
What we find especially interesting is that these traps are not merely optimization bugs. The same feature-learning pressures that help networks learn useful representations for the current task can also push them toward states with less future adaptability.
This raises a difficult open question for future work: are neural networks trained with SGD and cross-entropy loss fundamentally the right framework for continual learning?
Please read the full paper for more details: arxiv.org/pdf/2510.00304
Amir Joudaki@AmirJoudaki
Neural nets don’t just forget. Sometimes, after long training, they lose the ability to learn at all. In our #ICLR2026 poster, we model Loss of Plasticity as gradient dynamics trapped in invariant manifolds: 🔴 frozen units, 🔵 cloned units. The video makes the traps visible.
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