We submit MÉTRON-FR, a 125M GPT-2 pretrained on 92.50M words of French, to the BabyLM 2026 Strict track. It scores 85.97 ± 0.17% on QFrBLiMP and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol that combines French task-data translation with rank-16 LoRA produces a sharp task-type gradient: relational tasks gain measurably, while world-knowledge tasks regress. Bilingual Lexicon Induction aligns the French embeddings to GPT-2 at p@1 = 68.84 ± 8.61%, 32× above chance, suggesting cross-lingual alignment tracks acquired grammatical competence rather than training duration. An ablation study shows that single-token zero-shot scoring is dominated by tokenizer and template artifacts at the child scale, motivating tokenizer-swap sensitivity, placebo-controlled prompting, and native-language minimal-pair benchmarks as standard diagnostics.