On Understanding of the Dynamics of Model Capacity in Continual Learning

• URL: http://arxiv.org/abs/2508.08052v2
• Date: Thu, 14 Aug 2025 12:42:10 GMT
• Title: On Understanding of the Dynamics of Model Capacity in Continual Learning
• Authors: Supriyo Chakraborty, Krishnan Raghavan,
• Abstract summary: We introduce CL's effective model capacity that characterizes the dynamic behavior of the stability-plasticity balance point.<n>We show that regardless of the NN architecture or optimization method, a NN's ability to represent new tasks diminishes when incoming task distributions differ from previous ones.
• Score: 4.871035873389067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The stability-plasticity dilemma, closely related to a neural network's (NN) capacity-its ability to represent tasks-is a fundamental challenge in continual learning (CL). Within this context, we introduce CL's effective model capacity (CLEMC) that characterizes the dynamic behavior of the stability-plasticity balance point. We develop a difference equation to model the evolution of the interplay between the NN, task data, and optimization procedure. We then leverage CLEMC to demonstrate that the effective capacity-and, by extension, the stability-plasticity balance point is inherently non-stationary. We show that regardless of the NN architecture or optimization method, a NN's ability to represent new tasks diminishes when incoming task distributions differ from previous ones. We conduct extensive experiments to support our theoretical findings, spanning a range of architectures-from small feedforward network and convolutional networks to medium-sized graph neural networks and transformer-based large language models with millions of parameters.

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