Stability Does Not Mirror Plasticity: A Module-Level View of Declarative and Procedural Learning in Transformers

Plasticity and stability in continual learning are typically studied at the entire-model level and in a task-agnostic manner, yet prior work has reported divergent conclusions across settings. Inspired by cognitive distinctions between *declarative* and *procedural* memory, we instead study how plasticity and stability vary across transformer modules and task types. In a controlled synthetic environment with clean measurement, we show a double dissociation: query-key (QK) and value-output (VO) projections are the most plastic on procedural tasks, whereas MLPs are the most plastic on declarative memorization. Stability, however, does not simply mirror plasticity: when learning a new procedure, updating QK retains over half of the stored declarative memory, while updating MLPs retains far less and updating VO loses roughly four-fifths. Targeted module reset-and-recovery experiments explain this asymmetry: plasticity tracks which module is best suited to a task's primary computation or storage, whereas stability depends on all ways the existing capability relies on that module, including access to information stored in other modules. Experiments on a realistic model and tasks broadly echo these asymmetric dynamics. Overall, our work shows that plasticity and stability depend on different aspects of a module's functional role, offering a task-dependent, module-level perspective on continual learning.