Optimizer choice is a central hyperparameter in fine-tuning transformer models, yet its impact remains under-studied for Arabic-script social media classification un der class imbalance. We compare Adam, AdamW, and SGD for fine-tuning QARiB on two Arabic offensive-language bench marks, OffensEval20 and MPOLD, using a controlled grid over learning rate, weight decay, and warmup, and report test-set performance as mean (std) over three random seeds. Minority-class discrimination is evaluated using macro-F1 and AUC-PROFF, while calibration is assessed via expected calibration error (ECE), reliability diagrams, and proper scoring rules (Brier score and negative log-likelihood, NLL). Across both datasets, AdamW and Adam are consistently strong and closely matched when properly tuned, whereas SGD substantially underperforms under the same tuning bud get and exhibits higher seed sensitivity. We observe non-trivial miscalibration across optimizers; post-hoc temperature scaling offers a low-cost adjustment, yielding modest, dataset-dependent changes in calibration while preserving ranking-based discrimination. We further evaluate a practical decision-rule step by optimizing the classification threshold on the validation set and applying it to test predictions, and provide qualitative examples il lustrating typical optimizer-dependent confidence behaviors. In practice, for Arabic offensive-language detection under imbalance, we recommend starting from a tuned AdamW or Adam baseline; when calibrated probabilities are required for thresholding or triage, temperature scaling can be applied. We will release a reproducible pipeline to support further evaluation of optimizer–calibration trade-offs in Arabic-script safety tasks.