MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models
Provides a memory-efficient continual learning method for small language models in resource-constrained deployments, addressing a practical bottleneck where existing LLM-based approaches are infeasible.
MIITA enables continual learning for small language models under constrained storage by storing correction-direction prototypes and applying them at inference time via gated hidden-state adaptation, achieving consistent performance gains and reduced forgetting across diverse supervised CL settings.
Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However, directly updating their limited parameter space causes catastrophic forgetting. While memory-based methods naturally address this by decoupling knowledge retention from parameters, existing approaches designed for large language models (LLMs) rely on abundant storage and strong in-context reasoning that SLMs lack. To address these challenges, we propose MIITA, a Memory-Induced Inference-Time Adaptation framework for supervised CL under constrained storage. MIITA stores supervised experiences as compact correction-direction prototypes with semantic anchors, and retrieves them at inference time using semantic and uncertainty-based cues. The retrieved directions are applied through gated temporary hidden-state adaptation, enabling non-destructive reuse of past supervision without backbone updates, prompt extensions, or test-time backpropagation. A local theoretical analysis links this design to first-order loss reduction, uncertainty-guided retrieval, and directional coverage for retaining old-stage knowledge. Extensive experiments across diverse supervised CL settings show that MIITA consistently improves final performance and mitigates forgetting under fixed memory budgets.