Lifelong Normalization combined with ridge-regularized regression produces asymptotically orthogonal and bounded parameter updates that mitigate forgetting and collapse in lifelong model editing.
Consequently, we obtain the bound for the bias term: E[∥∆′bias t,l ∥2 F ]≤3γ 2L2 F n2 t (KΣ)1/4p 5Cϕ MSE(µ) t,l
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More Edits, More Stable: Understanding the Lifelong Normalization in Sequential Model Editing
Lifelong Normalization combined with ridge-regularized regression produces asymptotically orthogonal and bounded parameter updates that mitigate forgetting and collapse in lifelong model editing.