EWC stops only guarding old weights: FS-EWC shapes the solution first, then protects it, beating EWC across LoRA benchmarks
In replay-free continual learning, EWC penalizes changes to parameters deemed important using Fisher information estimated at the retained solution, but that local estimate does not capture sensitivity after the parameters move. FS-EWC uses Fisher information earlier, shaping the current task's solution by minimizing the worst-case Fisher trace over a neighborhood, and builds the retained anchor by averaging checkpoints along the optimization trajectory. Across vision and language benchmarks, FS-EWC generally improves on the corresponding EWC baseline under both full-parameter fine-tuning and LoRA.