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[2602.20791] Understanding the Role of Rehearsal Scale in Continual Learning under Varying Model Capacities
Summary
This paper explores the impact of rehearsal scale on continual learning, revealing counterintuitive effects on adaptability and memory retention in machine learning models.
Why It Matters
Understanding the role of rehearsal in continual learning is crucial for developing more effective machine learning algorithms. This research challenges traditional views, suggesting that larger rehearsal scales may not always enhance performance, which could influence future algorithm design and implementation.
Key Takeaways
- Rehearsal can negatively affect model adaptability contrary to common belief.
- Increasing rehearsal scale does not guarantee improved memory retention.
- The study provides a framework for analyzing rehearsal in continual learning.
- Insights were validated through simulations on deep neural networks.
- The findings highlight the complexity of rehearsal mechanisms in machine learning.
Computer Science > Machine Learning arXiv:2602.20791 (cs) [Submitted on 24 Feb 2026] Title:Understanding the Role of Rehearsal Scale in Continual Learning under Varying Model Capacities Authors:JinLi He, Liang Bai, Xian Yang View a PDF of the paper titled Understanding the Role of Rehearsal Scale in Continual Learning under Varying Model Capacities, by JinLi He and 2 other authors View PDF HTML (experimental) Abstract:Rehearsal is one of the key techniques for mitigating catastrophic forgetting and has been widely adopted in continual learning algorithms due to its simplicity and practicality. However, the theoretical understanding of how rehearsal scale influences learning dynamics remains limited. To address this gap, we formulate rehearsal-based continual learning as a multidimensional effectiveness-driven iterative optimization problem, providing a unified characterization across diverse performance metrics. Within this framework, we derive a closed-form analysis of adaptability, memorability, and generalization from the perspective of rehearsal scale. Our results uncover several intriguing and counterintuitive findings. First, rehearsal can impair model's adaptability, in sharp contrast to its traditionally recognized benefits. Second, increasing the rehearsal scale does not necessarily improve memory retention. When tasks are similar and noise levels are low, the memory error exhibits a diminishing lower bound. Finally, we validate these insights through numerical si...