# STABLE

Forgetting in continual learning is usually fought with replay, regularization or architectural changes; STABLE instead works inside the optimizer, treating the distance from the previous task's solution as a budget that tightens the pullback when exceeded and relaxes it once drift returns inside. On the 100-task ImageNet-R stream the same LoRA goes from 0.66% average accuracy with Adam to 73.12%, with average forgetting falling from 60.22% to 10.17%, and CLIP zero-shot transfer stays near its pre-trained level.

Forgetting in continual learning is usually fought with replay, regularization or architectural changes; STABLE instead works inside the optimizer, treating the distance from the previous task's solution as a budget that tightens the pullback when exceeded and relaxes it once drift returns inside. On the 100-task ImageNet-R stream the same LoRA goes from 0.66% average accuracy with Adam to 73.12%, with average forgetting falling from 60.22% to 10.17%, and CLIP zero-shot transfer stays near its pre-trained level.