This paper presents our 1st-place system for the Shared Task on Fine-Grained Toxicity Detection in Online Gaming (GameTox) at the 9th EEUCA Workshop, co-located with ACL 2026. The task targets 6-class fine-grained toxic intent classification on the official GameTox dataset, comprising 53,000 real-world World of Tanks chat utterances. We propose a three-stage progressive training framework built on XLM-RoBERTa-large: (1) gaming domain adaptive MLM pre-training, (2) multilingual toxicity transfer fine-tuning, and (3) supervised contrastive learning (SCL)-enhanced target task tuning. We further incorporate LLM-driven data augmentation and long-tailed class synthesis. Our system achieves a Macro F1 of 0.7041, ranking 1st among 35 teams. Ablation studies validate each module’s contribution, and we release our code to facilitate follow-up research.