This article discusses how fine-tuning open-source LLM judges using Direct Preference Optimization (DPO) can lead to performance that matches or exceeds GPT-5.2 in evaluating model outputs. The authors trained models like GPT-OSS 120B and Qwen 3 235B on human preference data, achieving better accuracy and efficiency at a lower cost.