Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. Existing parameter-efficient methods often limit model expressivity or introduce new parameters per task, creating scalab…