The increasing complexity of human language and the need for precise interpretation across various applications have posed significant challenges for language models, particularly in aligning their outputs with intended instructions. Addressing this issue through a novel back-and-forth weight propagation method has shown substantial promise in enhancing alignment accuracy, response coherence, and training efficiency. The proposed method leverages an iterative feedback mechanism that refines the model's internal representations, leading to more reliable and consistent outputs, even across diverse and complex instructional prompts. Experimental results demonstrated significant improvements in error rate reduction, scalability across different model sizes, and overall model fidelity. The findings suggest that this approach not only enhances the interpretative capabilities of language models but also offers practical benefits for resource-constrained environments, making it a valuable addition to both open source and commercial model development efforts. The research lays the groundwork for future advancements in model alignment, with implications for a wide range of real-world applications that require accurate and coherent language processing.