Fine-tuning improves measured accuracy on legal question answering, but does it improve how models use supplied law? We investigate this question in bilingual Bangladeshi legal QA, distinguishing gains due to scoring and retrieval from changes in reliance on the governing provision. We adapt six small instruction-tuned models with LoRA across three random seeds and evaluate them using constrained option-letter scoring, cyclic option rotation, and matched contexts that include or omit the governing provision. Scoring choices substantially change the apparent benefit of adaptation: a 50.0% gain under exact-line parsing falls to 3.0% under option scoring, and, for one model, the two methods reverse the ranking of systems. Supplied law improves accuracy, and removing the governing provision degrades performance for both base and adapted models. However, difference-in-differences estimates detect no increase in reliance on that provision after fine-tuning. These findings identify a limit to interpreting accuracy gains as evidence of legal adaptation: improved performance need not imply improved use of legal context. Evaluations must therefore distinguish scorer, retriever, and model effects. We release a hierarchy-preserving statutory corpus, 2,165 reviewed bilingual training examples, a 150-item supplied-law benchmark, and evaluation code at https://github.com/m-mahadi/bangladesh-legal-qa.