This submission describes the PingAn-NLP system for SemEval-2026 Task 9 Subtask 3, identifying polarization manifestations in 18 languages. We employ a tiered optimization framework integrating Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO). Key technical innovations include synthetic reasoning distillation from a 235B teacher model , a Smart-Tradeoff reward function designed to mitigate extreme label imbalance , and a tiered ensemble voting strategy that adaptively adjusts decision thresholds based on language resources. Our 8B-GRPO-Vote system demonstrated robust internal performance in tracks like English and Hindi and officially secured second place in the Bengali, English, Odia, and Turkish competitions.

This paper presents our solution for SemEval-2025 Task 2 on entity-aware machine translation. We propose a parameter-efficient adaptation framework using Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-72B model, enabling effective knowledge transfer while preserving generalization capabilities. To address data scarcity and entity ambiguity, we design a Wiki-driven augmentation pipeline that leverages Wikidata’s multilingual entity mappings to generate synthetic training pairs. Our system achieves state-of-the-art performance across 10 languages, securing first place in the competition. Experimental results demonstrate significant improvements in both translation quality (COMET) and entity accuracy (M-ETA).