The field of Large Language Models (LLMs) is witnessing significant advancements in reasoning capabilities. Recent developments focus on improving the models' ability to engage in complex reasoning tasks, such as debate, strategic planning, and meta-introspection. Researchers are exploring novel approaches to enhance LLMs' performance, including self-training, multimodal debate frameworks, and reflection-based learning. These innovations aim to address the challenges of scaling, oversight, and hallucinations in LLMs, ultimately leading to more reliable and generalizable reasoning capabilities. Noteworthy papers in this area include ReflAct, which introduces a novel backbone for world-grounded decision making, and ReflectEvo, which demonstrates the effectiveness of reflection learning for small LLMs. Additionally, papers like Self-Reasoning Language Models and Debate, Train, Evolve propose innovative frameworks for self-improvement and multimodal debate, showcasing substantial empirical gains and strong cross-domain generalization.