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[2502.07274] Forget Forgetting: Continual Learning in a World of Abundant Memory
Summary
The paper explores continual learning (CL) in AI, proposing a shift from minimizing memory usage to leveraging abundant memory while addressing the challenges of model stability and plasticity.
Why It Matters
This research is significant as it challenges traditional CL paradigms, emphasizing the need for cost-effective solutions in AI systems where memory is not a constraint. It offers a new method, Weight Space Consolidation, that enhances learning efficiency, which is crucial for advancing AI applications in real-world scenarios.
Key Takeaways
- Continual learning should focus on leveraging abundant memory rather than minimizing it.
- The core challenge in CL shifts from stability to plasticity as models adapt to new tasks.
- Weight Space Consolidation combines parameter resets and weight averaging for improved learning.
- The proposed method outperforms existing baselines while maintaining low computational costs.
- This research sets a new standard for practical CL systems in AI.
Computer Science > Machine Learning arXiv:2502.07274 (cs) [Submitted on 11 Feb 2025 (v1), last revised 18 Feb 2026 (this version, v5)] Title:Forget Forgetting: Continual Learning in a World of Abundant Memory Authors:Dongkyu Cho, Taesup Moon, Rumi Chunara, Kyunghyun Cho, Sungmin Cha View a PDF of the paper titled Forget Forgetting: Continual Learning in a World of Abundant Memory, by Dongkyu Cho and 4 other authors View PDF HTML (experimental) Abstract:Continual learning (CL) has traditionally focused on minimizing exemplar memory, a constraint often misaligned with modern systems where GPU time, not storage, is the primary bottleneck. This paper challenges this paradigm by investigating a more realistic regime: one where memory is abundant enough to mitigate forgetting, but full retraining from scratch remains prohibitively expensive. In this practical "middle ground", we find that the core challenge shifts from stability to plasticity, as models become biased toward prior tasks and struggle to learn new ones. Conversely, improved stability allows simple replay baselines to outperform the state-of-the-art methods at a fraction of the GPU cost. To address this newly surfaced trade-off, we propose Weight Space Consolidation, a lightweight method that combines (1) rank-based parameter resets to restore plasticity with (2) weight averaging to enhance stability. Validated on both class-incremental learning with image classifiers and continual instruction tuning with large lang...
