The field of large language model reasoning is moving towards more advanced and innovative methods, with a focus on reinforcement learning and verification techniques. Researchers are exploring new approaches to improve the reasoning capabilities of large language models, such as using generative models, self-rewarding mechanisms, and SAT-based reinforcement learning. These methods aim to address the limitations of current reinforcement learning tasks, including scalability, verifiability, and controllable difficulty. Notably, the development of new benchmarks, such as VerifyBench, is facilitating the evaluation and improvement of reference-based reward systems. Overall, the field is witnessing significant progress, with potential applications in various domains, including mathematics, coding, and machine translation. Some noteworthy papers include: SHARP, which introduces a unified approach to synthesizing high-quality aligned reasoning problems for large reasoning models. TinyV, which proposes a lightweight verifier to reduce false negatives in verification and improve RL training. General-Reasoner, which presents a novel training paradigm to enhance LLM reasoning capabilities across diverse domains.