arXiv 2505.099522025-05-15cs.LGcs.AI

面向长期持续学习的任务核心记忆管理与巩固

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

School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；Computation and Artificial Intelligence Innovative College, Fudan University（复旦大学计算与人工智能创新学院）

Tianyu Huai, Jie Zhou, Yuxuan Cai, Qin Chen, Wen Wu, Xingjiao Wu, Xipeng Qiu, Liang He

备注：: Submitted to Neurips2025

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AI总结：

针对任务数量庞大导致灾难性遗忘加剧的长期持续学习问题，提出受人类记忆启发的Long-CL框架，通过任务核心记忆管理与长期记忆巩固提升知识保持，并发布两项基准，AP较此前最优方法分别提升7.4%和6.5%。

摘要：

本文聚焦于长期持续学习（CL）任务：模型随时间从大量任务构成的序列流中依次学习，在获取新知识的同时保留先前学到的信息，其方式类似于人类学习。与传统CL设置不同，长期CL需要处理数量显著更多的任务，这会加剧灾难性遗忘问题。我们的工作旨在解决两个关键问题：1）现有CL方法在长期CL情境中的表现如何？2）如何缓解由长时间序列更新引发的灾难性遗忘？为应对这些挑战，我们受人类记忆机制启发，提出了一种用于长期持续学习的新颖框架Long-CL。具体而言，我们引入任务核心记忆管理策略，以高效索引关键记忆，并随着学习推进自适应地更新这些记忆。此外，我们开发了长期记忆巩固机制，该机制选择性保留困难且具有判别性的样本，从而确保稳健的知识保留。为推动该领域研究，我们构建并发布了多模态和文本两个基准，即MMLongCL-Bench和TextLongCL-Bench，为评估长期CL方法提供了宝贵资源。实验结果表明，Long-CL在两个基准上的AP分别比此前最先进方法高出7.4%和6.5%，证明了本方法的有效性。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。