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Towards understanding memory buffer based continual learning.

Guodong Zheng¹, Peng Wang², Tao Sun³

¹School of Mathematics and Statistics, Huazhong University of Science and Technology,China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 3, 2026

Summary

This summary is machine-generated.

Memory buffers in continual learning (CL) do not always improve performance. This research provides a theoretical framework showing when memory buffer-based CL (MCL) helps or hurts compared to training without a buffer (NCL).

Keywords:

Continual learning Forgetting Generalization error Memory buffer

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Area of Science:

Artificial Intelligence
Machine Learning
Theoretical Computer Science

Background:

Continual learning (CL) allows models to learn sequentially.
Memory buffer-based CL (MCL) is common but lacks theoretical understanding.
Existing research often focuses on CL without memory buffers.

Purpose of the Study:

To develop a theoretical framework for memory buffer-based CL (MCL).
To analyze the impact of memory buffers on forgetting and generalization.
To compare MCL with non-buffer CL (NCL).

Main Methods:

Derivation of expected forgetting and generalization error expressions.
Analysis of overparameterized linear models.
Coefficient-by-coefficient comparison of MCL and NCL.
Experimental validation using deep neural networks.

Main Results:

Memory buffers do not guarantee reduced forgetting or improved generalization.
MCL can be less efficient than NCL, leading to wasted resources.
When tasks are similar, increasing buffer size with model capacity reduces forgetting.
When tasks are dissimilar, MCL may perform worse than NCL.

Conclusions:

The effectiveness of memory buffers in CL depends on task similarity and model capacity.
Theoretical insights offer practical guidance for designing MCL algorithms.
Careful consideration of buffer usage is needed to avoid performance degradation and inefficiency.