This paper addresses the challenge of computational humor generation proposed in SemEval-2026 Task 1: Humor Generation. Our approach leverages Group Relative Policy Optimization, with an LLM serving as the policy and a custom joke rating model providing a reward signal. We demonstrate that this framework is an effective and computationally efficient approach, reliably producing genuinely funny content that adheres to task constraints.