Project Spillover: Quantifying the Alignment Tax

In the pursuit of AI safety, model alignment is often treated as a purely additive process—layering safety guards on top of intelligence. However, this view ignores the "Alignment Tax": the degradation of general reasoning capabilities caused by restrictive fine-tuning.

In this study, we treat the language model as a patient and the safety intervention as surgery. By performing a naive safety fine-tuning (LoRA) on GPT-2, we observed a catastrophic "capability spillover." While the model achieved a 100% refusal rate for harmful queries, it simultaneously lost basic arithmetic and coding abilities—a phenomenon we characterize as a digital lobotomy.

We utilize mechanistic interpretability to identify the specific internal circuits responsible for this collapse and propose future directions for more surgical alignment techniques.