aiqbench analysis
This paper is squarely about AI: it proposes using a reinforcement learning agent to set regularisation strengths per phase in class-incremental learning, a core continual-learning problem. The reported gains on CIFAR-100 and Tiny-ImageNet are modest in scope, and the claim that policies trained on short pseudo-task horizons transfer to longer sequences is the key result to verify against stronger baselines. For ML readers, the idea of meta-learning the stability-plasticity trade-off is worth watching, though the practical value depends on how well the approach holds up beyond these benchmarks.
REDAR: Reinforcement-Driven Adaptive Regularisation for Class Incremental Learning
arXiv:2610.31991v1 Announce Type: new Abstract: Regularisation-based class-incremental learning (CIL) methods control the stability-plasticity trade-off through fixed coefficients tuned offline, independently of the continual learning objective and blind to phase-specific variations in data volume, task difficulty, and class similarity. We argue this is a fundamental mismatch and propose REDAR: Reinforcement-Driven Adaptive Regularisation for Class Incremental Learning, which treats regularisation strength selection as a sequential decision problem. At each incremental phase, a lightweight reinforcement learning (RL) agent observes a compact state of current performance metrics and incoming data statistics, and outputs the distillation coefficients for that phase. To respect CIL's constraint on historical data access, the policy is trained entirely on pseudo-CIL tasks constructed from the initial phase, then deployed on the incremental sequence without further adaptation. Experiments on CIFAR-100 and Tiny-ImageNet demonstrate that REDAR reduces forgetting and improves forward transfer over fixed and heuristic baselines, and that policies trained on short pseudo-task horizons transfer effectively to longer incremental runs.
Abstract:Regularisation-based class-incremental learning (CIL) methods control the stability-plasticity trade-off through fixed coefficients tuned offline, independently of the continual learning objective and blind to phase-specific variations in data volume, task difficulty, and class similarity. We argue this is a fundamental mismatch and propose REDAR: Reinforcement-Driven Adaptive Regularisation for Class Incremental Learning, which treats regularisation strength selection as a sequential decision problem. At each incremental phase, a lightweight reinforcement learning (RL) agent observes a compact state of current performance metrics and incoming data statistics, and outputs the distillation coefficients for that phase. To respect CIL's constraint on historical data access, the policy is trained entirely on pseudo-CIL tasks constructed from the initial phase, then deployed on the incremental sequence without further adaptation. Experiments on CIFAR-100 and Tiny-ImageNet demonstrate that REDAR reduces forgetting and improves forward transfer over fixed and heuristic baselines, and that policies trained on short pseudo-task horizons transfer effectively to longer incremental runs.
Machine Learning (cs.LG)
arXiv:2610.31991 [cs.LG]
(or arXiv:2610.31991v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.31991 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 247 words