AI RESEARCH
Trust Region Continual Learning as an Implicit Meta-Learner
arXiv CS.LG
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ArXi:2602.02417v2 Announce Type: replace Continual learning aims to acquire tasks sequentially without catastrophic forgetting, yet standard strategies face a core tradeoff: regularization-based methods (e.g., EWC) can overconstrain updates when task optima are weakly overlapping, while replay-based methods can retain performance but drift due to imperfect replay. We study a hybrid perspective: \emph{trust region continual learning} that combines generative replay with a Fisher-metric trust region constraint.