这篇论文把持续学习的新玩法梳理清楚了，不再只盯着参数调整，连测试时训练、外部记忆都算进去了。搞AI系统的可以看看。
该论文从When、How、Where三个维度梳理持续学习（CL）的演进，指出其正从参数中心学习转向系统级适应。How维度涵盖off-policy、on-policy及超越梯度的优化机制；When维度横跨预训练、后训练与推理时阶段；Where维度区分内部参数更新与外部结构约束。论文系统综述了代表性方法，并讨论了这一范式转变的关键挑战与未来方向。
Continual Learning in Transition
Classical continual learning (CL) has primarily focused on enabling models to update and retain knowledge through parameter-centric mechanisms, e.g., training strategies, architectural designs, and weight adaptation. However, emerging paradigms are reshaping the scope of CL beyond this traditional model adaptation view. For instance, on-policy learning broadens the space of update mechanisms; test-time training extends CL from the training phase to inference; and external harness components such as memory, skill libraries, and interaction protocols extend the evolutionary boundaries of model capabilities far beyond the static parameter space. Collectively, these developments indicate a transition from parameter-centric learning toward system-level adaptation. To characterize this transition, we examine the evolution of continual learning through three dimensions: When, How, and Where learning occurs. The How dimension encompasses off-policy, on-policy, and beyond-gradient optimization mechanics. The When dimension captures evolution across pre-training, post-training, and inference-time stages. The Where dimension delineates updates occurring within internal parameters versus external structural constraints. Anchored by this tri-axial framework, we systematically survey representative methods, trace the ongoing transition of continual learning, and discuss the key challenges, broader implications, and future directions arising from this paradigm shift.