AI RESEARCH
Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning
arXiv CS.LG
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ArXi:2606.01379v1 Announce Type: new While prompt-based parameter-efficient continual learning mitigates catastrophic forgetting by isolating task-specific prompts, this isolation also limits later tasks from improving earlier ones, leaving backward knowledge transfer underexplored. We address this limitation by proposing Selective bAckward refinement for positive Backward knowledge transfER (SABER), a replay-free framework that enables controlled backward transfer in prompt-based continual learning.