AllocAdapt: Adaptive Training Budget Allocation for Continual Learning

Recent class-incremental learning (CIL) methods achieve strong performance by adapting pre-trained models with lightweight adapters and low-rank updates. However, their parameter efficiency does not necessarily translate into efficient training, as fixed schedules overlook differences in optimization demand across tasks and stages. We propose AllocAdapt, an adaptive update allocation framework that jointly addresses two complementary decisions: how much to update and where to allocate updates. HowMuch combines probe statistics with historical calibration to determine task- and stage-level training horizons under budget constraints. Within these horizons, Where allocates update opportunities through parameter-update routing or class sampling, according to each learner's optimization structure. Experiments on three recent parameter-efficient CIL methods and three image classification benchmarks show that AllocAdapt reduces training iterations by up to 80% while largely preserving average incremental accuracy relative to the original training procedures.