Task Switching Without Forgetting via Proximal Decoupling

TL;DR

This paper introduces a novel continual learning method that separates task learning from stability enforcement using operator splitting, leading to improved performance without replay buffers or meta-learning.

Contribution

It proposes a proximal decoupling approach that explicitly separates learning and stability, enhancing continual learning efficiency and effectiveness.

Findings

Achieves state-of-the-art results on standard benchmarks.

Improves stability and adaptability without replay buffers.

Provides theoretical justification for the splitting method.

Abstract

In continual learning, the primary challenge is to learn new information without forgetting old knowledge. A common solution addresses this trade-off through regularization, penalizing changes to parameters critical for previous tasks. In most cases, this regularization term is directly added to the training loss and optimized with standard gradient descent, which blends learning and retention signals into a single update and does not explicitly separate essential parameters from redundant ones. As task sequences grow, this coupling can over-constrain the model, limiting forward transfer and leading to inefficient use of capacity. We propose a different approach that separates task learning from stability enforcement via operator splitting. The learning step focuses on minimizing the current task loss, while a proximal stability step applies a sparse regularizer to prune unnecessary parameters and preserve task-relevant ones. This turns the stability-plasticity into a negotiated update between two complementary operators, rather than a conflicting gradient. We provide theoretical justification for the splitting method on the continual-learning objective, and demonstrate that our proposed solver achieves state-of-the-art results on standard benchmarks, improving both stability and adaptability without the need for replay buffers, Bayesian sampling, or meta-learning components.