The ability to control and align the outputs of advanced language models with predefined human preferences has become increasingly essential across various applications. Traditional methods that rely heavily on human feedback and expert reviews often face challenges related to scalability, subjectivity, and inefficiency, particularly when applied to largescale tasks. A novel approach, dense token masking, offers an automated solution that eliminates the need for human intervention, allowing the model to adhere closely to user-defined objectives through selective masking of tokens. This method enhances both accuracy and semantic consistency, achieving significant improvements in perplexity, token overlap, and error rates without imposing heavy computational costs. The research demonstrates that dense token masking presents a robust and adaptable alternative to traditional feedback-driven alignment techniques, making it well-suited for diverse applications where precision and scalability are critical. Results confirm the method's capacity to maintain output diversity while ensuring alignment with task-specific goals, positioning dense token masking as a key advancement in preference-controlled text generation.