CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework

• URL: http://arxiv.org/abs/2609.17026v1
• Date: Tue, 15 Sep 2026 11:33:44 GMT
• Title: CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework
• Abstract summary: Continual learning must balance the learning of new knowledge with the retention of previously learned knowledge.<n>Existing methods exhibit a scalability bottleneck when trained sequentially on many tasks.<n>Inspired by evidence that sparse fine-tuning achieves performance comparable to full fine-tuning, this paper presents a novel sparsity-driven continual learning framework.
• Score: 42.31039781559425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual learning must balance the learning of new knowledge with the retention of previously learned knowledge to incrementally learn tasks from a data stream without catastrophic forgetting. While leveraging pretrained models has significantly advanced continual learning, existing methods exhibit a scalability bottleneck when trained sequentially on many tasks, suffering from performance degradation due to inter-task interference and loss of plasticity. Inspired by evidence that sparse fine-tuning achieves performance comparable to full fine-tuning, this paper presents a novel sparsity-driven continual learning framework. Our continual learning method, termed CLARE, operates in two stages: it first identifies a sparse, task-critical parameter mask via a sparsity-inducing objective, then performs mask-constrained fine-tuning by only optimizing parameters selected by the mask. This two-stage sparse adapter mechanism enables all tasks to be accumulated within a shared adapter space while reducing destructive interference across tasks. Extensive experiments demonstrate the scalability of CLARE. On the long task-sequence benchmark Omnibenchmark-1k, CLARE outperforms strong baselines in final accuracy by a large margin, e.g, improving EASE by 4.64% and 13.34% after learning 100 tasks, respectively.

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