The field of large language model (LLM) reasoning is rapidly advancing, with a focus on developing more effective and efficient methods for training and evaluating these models. One of the key areas of research is the development of adaptive reasoning configurations, which can be used to improve the performance of LLMs on a wide range of tasks. Additionally, researchers are exploring new approaches to reinforcement learning, including the use of intrinsic motivation and process-level rewards, to improve the reasoning abilities of LLMs. These advances have the potential to enable LLMs to tackle more complex and nuanced tasks, and to improve their overall performance and robustness. Noteworthy papers include AdaReasoner, which introduces a plugin for automating adaptive reasoning configurations, and LeTS, which proposes a novel framework for learning to think and search via process-and-outcome reward hybridization. Maximizing Confidence Alone Improves Reasoning is also noteworthy as it proposes a fully unsupervised RL method that requires no external reward or ground-truth answers.