Learning Mamba as a Continual Learner

• URL: http://arxiv.org/abs/2412.00776v2
• Date: Mon, 03 Feb 2025 09:31:32 GMT
• Title: Learning Mamba as a Continual Learner
• Authors: Chongyang Zhao, Dong Gong,
• Abstract summary: We propose to meta-learn Mamba as a continual learner, referred to as MambaCL.<n>Our experiments and analyses highlight the promising performance and generalization capabilities of Mamba in meta-continual learning.
• Score: 12.697915176594314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual learning (CL) aims to efficiently learn and accumulate knowledge from a data stream with different distributions. By formulating CL as a sequence prediction task, meta-continual learning (MCL) enables to meta-learn an efficient continual learner based on the recent advanced sequence models, e.g., Transformers. Although attention-free models (e.g., Linear Transformers) can ideally match CL's essential objective and efficiency requirements, they usually perform not well in MCL. Considering that the attention-free Mamba achieves excellent performances matching Transformers' on general sequence modeling tasks, in this paper, we aim to answer a question -- Can attention-free Mamba perform well on MCL? By formulating Mamba with selective state space models (SSMs) for MCL tasks, we propose to meta-learn Mamba as a continual learner, referred to as MambaCL. By incorporating selectivity regularization, we can effectively train MambaCL. Through comprehensive experiments across various CL tasks, we also explore how Mamba and other models perform in different MCL scenarios. Our experiments and analyses highlight the promising performance and generalization capabilities of Mamba in MCL.

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