Task-Driven Subspace Decomposition for Knowledge Sharing and Isolation in LoRA-based Continual Learning

arXiv:2603.00191v4 Announce Type: replace Abstract: Continual Learning (CL) requires models to sequentially adapt to new tasks without forgetting old knowledge. Recently, Low-Rank Adaptation (LoRA), a representative Parameter-Efficient Fine-Tuning (PEFT) method, has gained increasing attention in CL. Several LoRA-based CL methods reduce interference across tasks by separating their update spaces, typically building the new space from the estimated null space of past tasks. However, they (i) overlook task-shared directions, which suppresses knowledge transfer, and (ii) fail to capture truly eff

Source: arXiv cs.LG — read the full report at the original publisher.

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