This paper describes our submission to the Japanese → English Article-level News Translation Task at WAT 2025. In this task, participants were provided with a small but high-quality parallel corpus along with two intermediate English translations: a literal translation and a style-adapted translation. To effectively exploit these limited training data, our system employs a large language model (LLM) trained via supervised fine-tuning (SFT) followed by Direct Preference Optimization (DPO) that is a preference learning technique for aligning model outputs with professional-quality references. By leveraging literal and style-adapted intermediate translations as negative (rejected) samples and human-edited English articles as positive (chosen) samples in DPO training, we achieved notable improvements in translation quality. We evaluate our approach using BLEU scores and human assessments.