FOREVER aligns replay intervals in LLM continual learning with a model-centric time based on optimizer update magnitudes and an Ebbinghaus-inspired forgetting curve to reduce catastrophic forgetting.
arXiv preprint arXiv:2301.12314 , year=
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Muon-OGD introduces a spectral-norm constrained orthogonal projection method solved via dual iterations and Newton-Schulz approximations to improve stability-plasticity trade-off in sequential LLM adaptation.
The paper reformulates industrial continual learning for LLMs as a closed-loop ecosystem problem, identifies three core challenges, and organizes solutions around five lifecycle design principles.
SETA decomposes parameters into task-specific and shared sparse experts with adaptive anchoring and routing regularization to improve retention and backward transfer in LLM continual learning.
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.