AI RESEARCH
One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning
arXiv CS.LG
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ArXi:2509.24483v3 Announce Type: replace Prompt-based methods have recently gained prominence in Continual Learning (CL) due to their strong performance and memory efficiency. A prevalent strategy in this paradigm assigns a dedicated subset of prompts to each task, which, while effective, incurs substantial computational overhead and causes memory requirements to scale linearly with the number of tasks. Conversely, approaches employing a single shared prompt across tasks offer greater efficiency but often suffer from degraded performance due to knowledge interference.