Weak-to-Strong Decoding (WSD) is a novel framework designed to enhance the alignment capabilities of large language models (LLMs) by utilizing a smaller aligned model to guide the initial drafting of responses. By integrating a well-aligned draft model, WSD significantly improves the quality of generated content while minimizing the alignment tax, as demonstrated through extensive experiments and the introduction of the GenerAlign dataset. The framework provides a structured approach for researchers to develop safe AI systems while navigating the complexities of preference alignment.