The field of large language models is moving towards more efficient and reliable reasoning capabilities. Recent developments have focused on optimizing test-time scaling, reducing computation overhead, and improving alignment strategies. Noteworthy papers include MUR, which proposes a momentum uncertainty-guided reasoning approach to reduce computation by over 50% while improving accuracy. URPO introduces a unified reward and policy optimization framework that simplifies the alignment pipeline and achieves superior performance. Other notable works, such as predictive scaling laws for GRPO training and group sequence policy optimization, demonstrate the potential for more efficient and effective training of large language models.