Ensembling large language models (LLMs) can effectively combine diverse strengths of different models, offering a promising approach to enhance performance across various tasks. However, existing methods typically rely on fixed weighting strategies that fail to adapt to the dynamic, context-dependent characteristics of LLM capabilities. In this work, we propose **R**einforcement **L**earning-**A**ssisted **E**nsemble for LLMs (RLAE), a novel framework that reformulates LLM ensemble through the lens of a Markov Decision Process (MDP). Our approach introduces a RL agent that dynamically adjusts ensemble weights by considering both input context and intermediate generation states, with the agent being trained using rewards that directly correspond to the quality of final outputs. We implement RLAE using both single-agent and multi-agent reinforcement learning algorithms (RLAE_PPO and RLAE_MAPPO ), demonstrating substantial improvements over conventional ensemble methods. Extensive evaluations on a diverse set of tasks show that RLAE outperforms existing approaches by up to 3.3\\% accuracy points, offering a more effective framework for LLM ensembling. Furthermore, our method exhibits superior generalization capabilities across different tasks without the need for retraining, while simultaneously achieving lower time latency. The source code is available at here.

Recent advances in large language models (LLMs) have significantly improved automated code generation, enabling tools such as GitHub Copilot and CodeWhisperer to assist developers in a wide range of programming tasks. However, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenging and underexplored area, especially as modern app interfaces become increasingly intricate. In this work, we propose UIOrchestra, a collaborative multi-agent system designed for the AppUI2Code task, which aims to reconstruct static single-page applications from design mockups. UIOrchestra integrates three specialized agents, layout description, code generation, and difference analysis agent that work collaboratively to address the limitations of single-model approaches. To facilitate robust evaluation, we introduce APPUI, the first benchmark dataset for AppUI2Code, constructed through a human-in-the-loop process to ensure data quality and coverage. Experimental results demonstrate that UIOrchestra outperforms existing methods in reconstructing complex app pages and highlight the necessity of multi-agent collaboration for this task. We hope our work will inspire further research on leveraging LLMs for front-end automation. The code and data will be released upon paper acceptance.