Team TüLK at SemEval-2026 Task 1: Humor Generation with Qwen and Group Relative Policy Optimization

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Team TüLK presented an innovative approach for SemEval-2026 Task 1, which challenges participants in computational humor generation. Their system employs Group Relative Policy Optimization (GRPO), a framework where a Large Language Model (LLM), specifically Qwen, serves as the core policy. This policy is guided by a custom joke rating model that provides a crucial reward signal, enabling the system to learn and refine its humor generation capabilities. The team demonstrated that this GRPO-based framework is both effective and computationally efficient. It reliably produces genuinely funny content that consistently adheres to the specific constraints outlined by the SemEval-2026 Task 1, showcasing a robust solution for automated humor creation.

Key takeaway

For Machine Learning Engineers developing creative text generation systems, consider Group Relative Policy Optimization (GRPO) with an LLM like Qwen. This approach, guided by a custom reward model, offers a computationally efficient and reliable method for generating constrained, genuinely funny content. You can adapt this framework to similar creative tasks requiring adherence to specific stylistic or thematic rules, potentially reducing manual content creation efforts.

Key insights

Group Relative Policy Optimization with an LLM and reward model effectively generates constrained humor.

Principles

• GRPO is effective for humor generation.
• LLMs can serve as policy in RL.
• Custom reward models guide complex tasks.

Method

Group Relative Policy Optimization uses an LLM (Qwen) as the policy, guided by a custom joke rating model providing a reward signal for humor generation.

In practice

• Automate humor content creation.
• Develop LLM-based policy optimization.
• Design custom reward models.

Topics

• Humor Generation
• SemEval-2026
• Group Relative Policy Optimization
• Large Language Models
• Qwen LLM
• Reward Models

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.