Task-Core Memory Management and Consolidation for Long-term Continual Learning

• URL: http://arxiv.org/abs/2505.09952v1
• Date: Thu, 15 May 2025 04:22:35 GMT
• Title: Task-Core Memory Management and Consolidation for Long-term Continual Learning
• Authors: Tianyu Huai, Jie Zhou, Yuxuan Cai, Qin Chen, Wen Wu, Xingjiao Wu, Xipeng Qiu, Liang He,
• Abstract summary: We focus on a long-term continual learning (CL) task, where a model learns sequentially from a stream of vast tasks over time.<n>Unlike traditional CL settings, long-term CL involves handling a significantly larger number of tasks, which exacerbates the issue of catastrophic forgetting.<n>We propose a novel framework inspired by human memory mechanisms for long-term continual learning (Long-CL)
• Score: 62.880988004687815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we focus on a long-term continual learning (CL) task, where a model learns sequentially from a stream of vast tasks over time, acquiring new knowledge while retaining previously learned information in a manner akin to human learning. Unlike traditional CL settings, long-term CL involves handling a significantly larger number of tasks, which exacerbates the issue of catastrophic forgetting. Our work seeks to address two critical questions: 1) How do existing CL methods perform in the context of long-term CL? and 2) How can we mitigate the catastrophic forgetting that arises from prolonged sequential updates? To tackle these challenges, we propose a novel framework inspired by human memory mechanisms for long-term continual learning (Long-CL). Specifically, we introduce a task-core memory management strategy to efficiently index crucial memories and adaptively update them as learning progresses. Additionally, we develop a long-term memory consolidation mechanism that selectively retains hard and discriminative samples, ensuring robust knowledge retention. To facilitate research in this area, we construct and release two multi-modal and textual benchmarks, MMLongCL-Bench and TextLongCL-Bench, providing a valuable resource for evaluating long-term CL approaches. Experimental results show that Long-CL outperforms the previous state-of-the-art by 7.4\% and 6.5\% AP on the two benchmarks, respectively, demonstrating the effectiveness of our approach.

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