The recent developments in the field of large language models (LLMs) have primarily focused on enhancing their reasoning capabilities, particularly in complex and multi-step tasks. A significant trend is the introduction of novel frameworks and methodologies aimed at improving the efficiency and accuracy of reasoning processes. These advancements include the use of compressed reasoning chains, entropy-regularized reward models, and retrieval-augmented verification to guide deliberative reasoning. Additionally, there is a growing emphasis on integrating external knowledge and active retrieval mechanisms to support multimodal reasoning tasks. The field is also witnessing innovations in fine-tuning techniques, such as solution guidance fine-tuning, which enhances the reasoning abilities of smaller models with minimal data. Furthermore, the development of feedback-free reflection mechanisms and meta-reflection frameworks is addressing the limitations of traditional iterative refinement processes. These approaches not only improve the models' performance but also make them more practical for real-world applications by reducing computational costs and latency. Notably, the integration of argumentation theory through critical questions is steering LLMs towards more robust and logical reasoning, particularly in mathematical and logical tasks. Overall, the research direction is moving towards more efficient, knowledge-augmented, and interpretable reasoning models that can handle complex tasks with greater accuracy and reliability.