Paradigm Shift in Continual Learning: From Parameter-Centric to System-Level Adaptation
CASIA · hf · 2026-08-07
This paper explores a significant paradigm shift in continual learning (CL), transitioning from traditional parameter-centric adaptation towards broader system-level adaptation.
- Traditional Limits: Classical methods relied heavily on parameter adjustments, architectural designs, and training strategies.
- Emerging Mechanisms: On-policy learning expands update spaces, test-time training extends CL into inference, and external harnesses (like memory and skill libraries) break static parameter boundaries.
- Tri-Axial Framework: The authors analyze this evolution across three dimensions:
- How: Encompassing off-policy, on-policy, and beyond-gradient optimization.
- When: Capturing evolution across pre-training, post-training, and inference-time stages.
- Where: Delineating updates within internal parameters versus external structural constraints.
Anchored by this framework, the paper surveys representative methods and discusses key challenges and future directions arising from this transition.
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