Large Language Models (LLMs) have taken the world by storm, generating human-like text and even tackling complex problems. But how far can their reasoning abilities truly go? One popular technique, Retrieval-Augmented Generation (RAG), empowers LLMs by connecting them to external databases. Instead of relying solely on their internal knowledge, these "plugged-in" LLMs can access and process information from vast external resources. It seems like the perfect solution for creating truly knowledgeable AI. However, new research reveals the limitations of RAG, suggesting that it doesn't automatically translate to deeper reasoning abilities. Think of reasoning as building a tower of logic, where each level represents a step in the thought process. While RAG initially seems like it could help LLMs build much taller towers by giving them access to new materials (information), the study finds it mostly helps widen the base rather than building higher. In essence, RAG is better at adding breadth of knowledge than depth of understanding. This limitation comes from several factors. First, simply retrieving relevant information isn't enough—LLMs need to process it, and this process itself requires reasoning. Imagine having all the right books but no time to actually read them. Second, real-world information sources aren't perfectly organized like a textbook. They contain noise and irrelevant details, which can distract the LLM. It’s like trying to find a specific fact in a cluttered library. Even when LLMs try to filter this information, they often struggle to do so efficiently without several additional steps, hindering their ability to reach truly advanced levels of reasoning. The study also dives into an intriguing technical challenge of noise filtering called the "triple-wise problem." Standard LLM architecture, based on pairwise relationships, makes filtering complex noise difficult, potentially requiring many extra layers of computation. The researchers propose a novel approach called "DPrompt tuning" to address this challenge. By pre-processing the retrieved information into a concise summary, they effectively streamline the filtering process. This method helps the LLM separate the valuable information from the noise more efficiently, boosting its ability to use external resources effectively. The findings highlight a critical distinction: adding more information doesn't automatically equate to better reasoning. Future research might explore smarter ways to combine RAG with techniques like Chain of Thought prompting, which focuses on step-by-step reasoning. The goal is to help LLMs not just access information but truly learn from it, developing deeper reasoning abilities essential for complex real-world applications. The study also highlights the need for improvement in information filtering. DPrompt tuning is a promising step toward creating a system for LLMs to truly think more deeply, opening new doors for creating truly knowledgeable and insightful AI systems.