Recent advancements in artificial intelligence have underscored the importance of custom reasoning models, known as Reasoning Language Models (RLMs), which are designed to perform complex, multi-step reasoning tasks with greater accuracy and interpretability than standard Large Language Models (LLMs). Unlike LLMs that rely on intuitive, rapid reasoning, RLMs employ deliberate cognitive processes, often using techniques like tree-search algorithms and reinforcement learning to navigate challenging scenarios and produce contextually accurate solutions. These models benefit from high-performance computing advancements and require significant computational resources due to their iterative and exploratory inference processes. While custom reasoning models offer improved accuracy and the ability to extrapolate beyond known contexts, they also present challenges such as computational complexity, the need for high-quality training data, and sophisticated evaluation methods. Effective deployment involves strategic infrastructure decisions, computational efficiency considerations, and ensuring data security, while future directions in the field may focus on automating fine-tuning processes, enhancing reinforcement learning methods, and exploring hybrid architectures that blend explicit reasoning with intuitive pattern-matching.