AI 学习不再只靠改参数，整个系统都在进化
我们一直以为 AI 的「持续学习」就是不断调整内部参数，像人记新知识。但新综述指出，这个时代正在过去：学习可以发生在训练时、推理时，甚至模型之外的记忆库、技能库和交互规则里。研究者用「何时、如何、何处」三个轴重新梳理了这条进化线，发现前沿已经从「改参数」转向「改系统」。它不是你明天能用上的东西，但它告诉你：AI 的下一个能力跃迁，可能不再靠更大的模型，而是靠更聪明的「外部装备」和「学习时机」。
📄 原文摘要(英文)
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.