AI RESEARCH
Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts
arXiv CS.LG
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ArXi:2602.03473v2 Announce Type: replace Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research interest in recent years. However, how to effectively learn both discriminative and comprehensive feature representations while maintaining stability and plasticity over very long task sequences remains an open problem. We propose CaRE, a scalable {C}ontinual Le{a}rner with efficient Bi-Level {R}outing Mixture-of-{E}xperts (BR-MoE