The field of large language model reasoning is rapidly evolving, with a focus on developing more effective and efficient methods for training and evaluating these models. A key area of research is the use of reward models to guide the reasoning process, with several papers proposing new architectures and training methodologies for these models. Another important direction is the development of more interpretable and explainable models, with techniques such as inverse reinforcement learning and code-based reward functions being explored. Additionally, there is a growing interest in multi-turn reasoning and the use of feedback to improve model performance. Notable papers in this area include S2J, which proposes a novel approach to bridging the gap between solving and judging ability in generative reward models, and SPARK, which introduces a synergistic policy and reward co-evolving framework for efficient and stable training. Other noteworthy papers include Conditional Advantage Estimation for Reinforcement Learning, which amplifies the impact of target metrics without presuming their direction, and In-Place Feedback, which offers a more natural and effective mechanism for guiding large language models in reasoning-intensive tasks.