The field of large language models (LLMs) is witnessing significant advancements in reasoning capabilities, with a focus on improving performance, efficiency, and adaptability. Recent developments have led to the creation of novel frameworks, such as asymmetric two-stage reasoning and composite reasoning, which enable LLMs to dynamically explore and combine multiple reasoning styles. These innovations have resulted in substantial performance improvements, with some models achieving state-of-the-art results on various benchmarks. Furthermore, research has highlighted the importance of data quality, reasoning intensity, and model architecture in shaping LLM reasoning behavior. Noteworthy papers include A2R, which presents a plug-and-play parallel reasoning framework that enhances model capabilities on complex questions, and Socratic-Zero, which introduces a fully autonomous framework for generating high-quality training data through agent co-evolution. Additionally, the Apriel-1.5-15B-Thinker model demonstrates competitive results without reinforcement learning or preference optimization, isolating the contribution of data-centric continual pre-training approaches.