Trust Region Continual Learning as an Implicit Meta-Learner

The paper introduces Trust Region Continual Learning, a hybrid approach that blends generative replay with a Fisher-metric trust region constraint to address catastrophic forgetting in sequential task learning. It demonstrates that, under local approximations, the update can be interpreted as a single-step Model-Agnostic Meta-Learning (MAML) process, where replay provides an old-task gradient and the Fisher penalty shapes curvature. Experiments on task-incremental diffusion image generation and continual diffusion-policy control show that this method achieves superior final performance, better retention, and faster early-task recovery compared to EWC, replay, and other continual meta-learning baselines.

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• diffusion