Generating high-quality PowerPoint slides from natural language instructions is a complex task that demands not only deep semantic understanding, but also aesthetic design. The essential component for building a functional and visually rich slide is the XML object. Therefore, it is intuitive that the most direct path to creating a high-quality slide is to generate it directly from the foundational XML structure. However, previous ``human instruction to slide generation" models typically rely on generating Python code, which serves as an intermediary to produce the final slide output rather than direct production of the XML object. As a result, these models lack the ability to precisely construct and control the building blocks required for a detailed slide composition. We introduce SlideTuner, a custom finetuned GPT-4o model specifically engineered to generate high-quality PowerPoint slides by generating the required XML files. Through extensive empirical experiments, we demonstrate that the fine-tuned GPT-4o model successfully and consistently produces visually coherent and aesthetically pleasing slides. The SlideTuner employs a two-stage training approach: first we apply SFT to the language model, enabling it to generate slide-rendering XML code directly from user instruction, utilizing XML data extracted from native PowerPoint slides. Second, we apply Direct Preference Optimization (DPO) to align the model's outputs with preferred visual styles, such as specific font choices. The slides produced by our model exhibit superior layout scores and style adherence. While this work focuses on font-level aesthetic control, our work establishes a foundation for future research aimed at precisely guiding slide generation toward diverse visual or structural preferences.