Humor generation presents significant challenges due to subjectivity and the limitations of automatic metrics. In this work, we address Task 1 of SemEval 2026 (Subtask A) by evaluating three instruction-tuned models (Llama 3.1, Gemma 2, and Qwen 2.5) via a round-robin LLM judging framework. We investigate the impact of Retrieval-Augmented Generation and Direct Preference Optimization (DPO) on performance. Our results identify Llama 3.1 as the strongest baseline and demonstrate that DPO consistently improves humor quality across configurations. These findings confirm the efficacy of LLM-based judging as a practical training signal for optimizing subjective generation tasks.