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.
arXiv preprint arXiv:2501.16372 , year=
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Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning
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.