Modern Standard Arabic grammatical error correction (GEC) remains underexplored for open-weight instruction-tuned large language models (LLMs). We compare natural, synthetic, and mixed training views for a highlighted-token correction task while holding the model, training-set size, and evaluation protocol fixed. We fine-tune Gemma 3 4B with QLoRA on three equal 2,000-record views and evaluate once on 511 Nahw-Passage records. Mixed supervision reaches 31.70% exact-match accuracy, compared with 20.55% for synthetic-only and 28.38% for natural-only; the mixed–natural difference is not established. Post-hoc robustness analyses preserve the mixed-over-synthetic direction, but checkpoint selection enlarges its measured size. Auxiliary diagnostics reveal a trade-off: synthetic-only changes fewer already-correct tokens, whereas mixed supervision retains more ArabicMMLU capability. The result concerns these particular data views: source also varies with domain, length, and error distribution; correction quality was not independently assessed. One model and strict single-reference scoring further limit generality.