The field of mathematical reasoning and large language models is rapidly advancing, with a focus on improving the ability of models to reason and solve complex mathematical problems. Recent research has explored the use of self-play, reinforcement learning, and multimodal learning to enhance the reasoning capabilities of large language models. One of the key challenges in this area is the development of robust evaluation metrics and benchmarks that can accurately assess the mathematical reasoning abilities of models. To address this challenge, researchers have proposed new benchmarks and evaluation frameworks that target the level of the International Mathematical Olympiad (IMO) and provide a more comprehensive assessment of mathematical reasoning capabilities. Another important area of research is the development of methods for generating high-quality mathematical problems and questions, including the use of collaborative multi-agent frameworks and difficulty-controllable generation models. Overall, the field is moving towards the development of more advanced and robust mathematical reasoning models that can solve complex problems and provide accurate and reliable results. Noteworthy papers in this area include: OpenSIR, which presents a self-play framework for open-ended mathematical discovery, and RIDE, which proposes a novel adversarial question-rewriting framework for evaluating mathematical reasoning ability. SAIL-RL is also a notable work, introducing a reinforcement learning post-training framework that enhances the reasoning capabilities of multimodal large language models.