This study introduces a new approach to enhance information retrieval accuracy in Large Language Models (LLMs) by integrating a specially designed reinforcement learning algorithm into the LLaMA model. The research focuses on developing and implementing an algorithm that dynamically adapts the model's response strategies to user queries, based on a combination of dynamical systems theory and relativistic physics. Empirical results demonstrate that the Optimized LLaMA model exhibits significant improvements in accuracy, relevance, and coherence across various information retrieval tasks compared to the Baseline LLaMA. This advancement not only showcases the potential of reinforcement learning in the realm of natural language processing but also marks a considerable step forward in the development of AI systems capable of nuanced language understanding and decision-making. The study's findings have profound implications for the future of AI research, particularly in enhancing the practical applicability of LLMs in complex, real-world scenarios, and set a new benchmark for the integration of machine learning techniques in language models.