Online continual learning (OCL) aims to train neural networks incrementally from a non-stationary data stream with a single pass through data. Rehearsal-based methods attempt to approximate the observed input distributions over time with a small memory and revisit them later to avoid forgetting. Despite its strong empirical performance, rehearsal methods still suffer from a poor approximation of the loss landscape of past data with memory samples. This paper revisits the rehearsal dynamics in online settings. We provide theoretical insights on the inherent memory overfitting risk from the viewpoint of biased and dynamic empirical risk minimization, and examine the merits and limits of repeated rehearsal. Inspired by our analysis, a simple and intuitive baseline, Repeated Augmented Rehearsal (RAR), is designed to address the underfitting-overfitting dilemma of online rehearsal. Surprisingly, across four rather different OCL benchmarks, this simple baseline outperforms vanilla rehearsal by 9%-17% and also significantly improves state-of-the-art rehearsal-based methods MIR, ASER, and SCR. We also demonstrate that RAR successfully achieves an accurate approximation of the loss landscape of past data and high-loss ridge aversion in its learning trajectory. Extensive ablation studies are conducted to study the interplay between repeated and augmented rehearsal and reinforcement learning (RL) is applied to dynamically adjust the hyperparameters of RAR to balance the stability-plasticity trade-off online.

本论文重新审视了在线学习中排挤记忆（rehearsal）动态。我们从偏差和动态经验风险最小化的角度提供了理论见解，并检查了重复练习的优点和局限性。受我们的分析启发，设计了一个简单直观的“重复增强排挤（Repeated Augmented Rehearsal，RAR）”基线，以解决在线排练的欠拟合和过拟合问题。该论文还展示了RAR在学习轨迹中成功实现了对过去数据损失景观和高损失梁脊的准确近似。我们通过广泛的消融研究研究了重复和增强练习之间的相互作用，同时应用强化学习（RL）来动态调整RAR的超参数，以在线平衡稳定性-可塑性权衡。

在线持续学习的简单而强大的基线：重复增强训练