保留还是适应？泛化持续学习
To Retain or to Adapt? Generalizing Continual Learning
AI总结：
该研究挑战持续学习以保留知识为中心的前提，将其形式化为在线优化问题，引入转移效率指标，得出关键任务持续时间，验证理论预测，还提出预测持续学习算法类别及窗口算法，在受控分布漂移下有良好表现。
摘要：
持续学习（CL）文献长期以来一直以减轻灾难性遗忘为目标。这一目标基于一个普遍且常未明确说明的假设：终身学习者应近似联合任务学习（JTL）解决方案并保留所有先前获得的知识。我们挑战这一以保留为中心的前提，认为在非平稳环境中优先保留会阻碍实时适应。将重点转移到平均终身误差（ALE），我们将CL形式化为一个由环境和学习动态交互控制的在线优化问题。我们引入转移效率作为衡量不稳定性（从冲突的过去经验继承的偏差）和瞬态误差（从头学习新任务的优化成本）之间紧张关系的定量指标。在温和的收敛条件下，这种分解产生一个关键任务持续时间：一个封闭形式的阈值，超过该阈值，每当保留引起正的平稳偏差时，历史知识就从热启动优势转变为优化负担。我们在持续图像分类和强化学习基准上验证了这些理论预测。最后，通过将持续学习与可预测序列的在线学习框架联系起来，我们表明JTL只是更广泛目标家族的一个实例，并提出了一种新的持续学习算法类别，称为预测持续学习。预测CL算法在明确的、动态更新的未来任务模型下优化预期未来性能。作为概念验证，我们分析了一种在JTL和独立任务学习（ITL）之间插值的窗口算法，在受控分布漂移下优于两者。
英文摘要：
The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting. This objective rests on a pervasive, often unstated assumption: that a lifelong learner should approximate the Joint-Task Learning (JTL) solution and retain all previously acquired knowledge. We challenge this retention-centered premise, arguing that in non-stationary environments prioritizing retention can impede real-time adaptation. Shifting the focus to the Average Lifelong Error (ALE), we formalize CL as an online optimization problem governed by the interaction between environmental and learning dynamics. We introduce Transfer Efficiency as a quantitative measure of the tension between Instability, the bias inherited from conflicting past experience, and Transient Error, the optimization cost of learning new tasks from scratch. Under mild convergence conditions, holding across linear and neural network models, this decomposition yields a Critical Task Duration: a closed-form threshold beyond which historical knowledge transitions from a warm-start advantage to an optimization liability whenever retention induces a positive stationary bias. We validate these theoretical predictions on continual image classification and reinforcement learning benchmarks. Finally, by connecting continual learning to the online learning framework of predictable sequences, we show that JTL is only one instance of a broader family of objectives, and we propose a new general class of continual learning algorithms, which we call Predictive Continual Learning. Predictive CL algorithms optimize expected future performance under an explicit, dynamically updated model of future tasks. As a proof of concept, we analyze a Window algorithm that interpolates between JTL and Independent-Task Learning (ITL), outperforming both under controlled distributional drift.
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