When Does Continual Learning Require Learning

As large language models (LLMs) become increasingly capable, the next question is how to enable them to continually learn. Today, the field largely frames this as a problem of context management and mitigating forgetting. We argue this framing is incomplete: continual learning is fundamentally about increasing model competence as the world changes. We disentangle this change along two axes: space, where the model encounters new domains, and time, where the underlying data drifts under a fixed task. We introduce a common framework that treats continual learning methods as update operators acting on model state, allowing weight updates, prompt optimization, retrieval, and context compression to be studied together. We operationalize this framework through sequential evaluations built from existing benchmarks and public datasets, comparing supervised learning, reinforcement learning, prompt-based methods, context compression, and retrieval. Our experiments with Qwen3-8B reveal distinct requirements for adaptation across settings. Under domain shift, self-distillation achieves higher final average performance than prompt-based methods. For changing factual knowledge, retrieval that replaces outdated records outperforms the evaluated learning methods without updating model weights, but this advantage degrades when conflicting versions accumulate. Under financial temporal drift, SDPO improves economic performance over the frozen model, while accuracy alone obscures differences in useful adaptation. These findings make the distinction between storing new information and learning from it central to continual learning. More broadly, our framework connects the choice of update mechanism to the structure of how data changes. We hope that understanding where each method succeeds and fails will guide the design of stronger continual learning systems.