LoRA Eliminates Catastrophic Forgetting: Comprehensive Empirical Study with 234 Experiments Validating Frozen-Weight Continual Learning Architecture

Catastrophic forgetting prevents continual learning in neural networks. We discover that Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, reduces catastrophic forgetting by 95.5% (from 20.8% to 0.9%, p=0.0028). Through 234 systematic experiments across 8 random seeds on BERT-base, we validate LoRA ranks (4, 8, 16, 32), quantization levels (4-bit, 8-bit), and module selections. Combined with 4-bit quantization, our best configuration achieves 72.2% accuracy, 0.46GB memory (83% reduction), and 1.4% forgetting (93.1% reduction). Freezing ablation studies validate the frozen-backbone mechanism. Code and data available on GitHub.