Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning 事件

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning arXiv:2606.07500v1 Announce Type: cross Abstract: Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities. We introduce Mixture of Sparse Experts for Task