Problem
Research questions and friction points this paper is trying to address.
Continual Learning
Parameter-Centric Learning
System-Level Adaptation
On-Policy Learning
Test-Time Training
Continual Learning in Transition
🤖 AI Summary
Traditional continual learning is constrained by a parameter-centric paradigm, limiting its capacity to meet system-level adaptation demands in dynamic environments. This work proposes a “Tri-Axis Framework” (When, How, Where), offering a unified perspective that reorients continual learning beyond mere parameter updates toward external architectures and inference-time adaptation. By integrating off-policy/on-policy learning, test-time training, external memory systems, and skill repositories, the framework transcends the limitations of static parameter spaces and gradient-based optimization. A systematic review elucidates the field’s evolutionary trajectory and highlights pivotal challenges and future directions inherent in this paradigm shift.
📝 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 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
continual learning
system-level adaptation
on-policy learning
test-time training
external memory
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