UI-UG: A Unified MLLM for UI Understanding and Generation

• URL: http://arxiv.org/abs/2509.24361v2
• Date: Tue, 30 Sep 2025 07:45:11 GMT
• Title: UI-UG: A Unified MLLM for UI Understanding and Generation
• Authors: Hao Yang, Weijie Qiu, Ru Zhang, Zhou Fang, Ruichao Mao, Xiaoyu Lin, Maji Huang, Zhaosong Huang, Teng Guo, Shuoyang Liu, Hai Rao,
• Abstract summary: We introduce UI-UG (a unified MLLM for UI Understanding and Generation), integrating both capabilities.<n>For understanding tasks, we employ Supervised Fine-tuning (SFT) combined with Group Relative Policy Optimization (GRPO) to enhance fine-grained understanding.<n>For generation tasks, we further use Direct Preference Optimization (DPO) to make our model generate human-preferred UIs.
• Score: 19.7078650905834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although Multimodal Large Language Models (MLLMs) have been widely applied across domains, they are still facing challenges in domain-specific tasks, such as User Interface (UI) understanding accuracy and UI generation quality. In this paper, we introduce UI-UG (a unified MLLM for UI Understanding and Generation), integrating both capabilities. For understanding tasks, we employ Supervised Fine-tuning (SFT) combined with Group Relative Policy Optimization (GRPO) to enhance fine-grained understanding on the modern complex UI data. For generation tasks, we further use Direct Preference Optimization (DPO) to make our model generate human-preferred UIs. In addition, we propose an industrially effective workflow, including the design of an LLM-friendly domain-specific language (DSL), training strategies, rendering processes, and evaluation metrics. In experiments, our model achieves state-of-the-art (SOTA) performance on understanding tasks, outperforming both larger general-purpose MLLMs and similarly-sized UI-specialized models. Our model is also on par with these larger MLLMs in UI generation performance at a fraction of the computational cost. We also demonstrate that integrating understanding and generation tasks can improve accuracy and quality for both tasks. Code and Model: https://github.com/neovateai/UI-UG

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