Trust Region Continual Learning as an Implicit Meta-Learner

• URL: http://arxiv.org/abs/2602.02417v1
• Date: Mon, 02 Feb 2026 18:19:16 GMT
• Title: Trust Region Continual Learning as an Implicit Meta-Learner
• Authors: Zekun Wang, Anant Gupta, Christopher J. MacLellan,
• Abstract summary: We study a hybrid perspective: emphtrust region continual learning that combines generative replay with a Fisher-metric trust region constraint.<n>We show that, under local approximations, the resulting update admits a MAML-style interpretation with a single implicit inner step.<n>This yields an emergent meta-learning property in continual learning.
• Score: 3.705371747297478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We show that, under local approximations, the resulting update admits a MAML-style interpretation with a single implicit inner step: replay supplies an old-task gradient signal (query-like), while the Fisher-weighted penalty provides an efficient offline curvature shaping (support-like). This yields an emergent meta-learning property in continual learning: the model becomes an initialization that rapidly \emph{re-converges} to prior task optima after each task transition, without explicitly optimizing a bilevel objective. Empirically, on task-incremental diffusion image generation and continual diffusion-policy control, trust region continual learning achieves the best final performance and retention, and consistently recovers early-task performance faster than EWC, replay, and continual meta-learning baselines.

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