Continual learning (CL) research typically assumes highly constrained exemplar memory resources. However, in many real-world scenarios--especially in the era of large foundation models--memory is abundant, while GPU computational costs are the primary bottleneck. In this work, we investigate CL in a novel setting where exemplar memory is ample (i.e., sufficient exemplar memory). Unlike prior methods designed for strict exemplar memory constraints, we propose a simple yet effective approach that directly operates in the model's weight space through a combination of weight resetting and averaging techniques. Our method achieves state-of-the-art performance while reducing the computational cost to a quarter or third of existing methods. These findings challenge conventional CL assumptions and provide a practical baseline for computationally efficient CL applications. Continual learning (CL) has attracted significant attention as a paradigm enabling machine learning models to adapt to sequential tasks while overcoming catastrophic forgetting of previously acquired knowledge (Wang et al., 2024).