持续学习的转型：从参数中心化到系统级适应

AI 摘要

本文提出持续学习正从参数中心化向系统级适应转变，通过When、How、Where三个维度分析这一演进。论文系统梳理了代表性方法，并讨论了该范式转变带来的挑战与未来方向。

正文节选

Continual Learning in Transition Abstract 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 infer

发布时间：—

抓取时间：2026-08-07 17:32

来源机构：Hugging Face