Same Methods, Different Rankings: Trainable Depth as an Evaluation Variable in Continual Learning
Continual learning (CL) examines how models learn a sequence of tasks while retaining previously learned knowledge. Despite substantial progress in benchmarking CL methods, comparative evaluations typically keep the fine-tuning regime fixed. In this paper, we argue that the fine-tuning regime, defined by the trainable parameter subspace, is itself a key evaluation variable. We formalize adaptation regimes as projected optimization over fixed trainable subspaces, showing that changing the trainable depth alters the effective update signal through which both current task fitting and knowledge preservation operate. This analysis motivates the hypothesis that method comparisons need not be in...
| Authors | Paul-Tiberiu Iordache, Elena Burceanu, Mihai Dascalu |
|---|---|
| Primary category | cs.LG |
| Published | 23 April 2026 |
| Last updated at source | 5 October 2026 |
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