The field of large language models (LLMs) is moving towards improving their reasoning capabilities, with a focus on developing more efficient and effective training strategies. One of the key directions is the use of reinforcement learning (RL) to enhance LLM reasoning, including the development of new frameworks and techniques such as adaptive logic blocks and unified fine-tuning. Another area of research is the analysis of structural assumptions underlying RL-based post-training methods, which has led to a better understanding of the limitations and potential biases of these approaches. Additionally, there is a growing interest in applying LLMs to educational settings, with a focus on developing effective tutoring systems that can provide pedagogical support and guided problem-solving. Overall, the field is witnessing significant innovations in LLM reasoning, with potential applications in various areas such as education and decision-making. Noteworthy papers include: UFT: Unifying Supervised and Reinforcement Fine-Tuning, which proposes a novel post-training paradigm that unifies supervised and reinforcement fine-tuning. RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning, which introduces a lightweight navigator model that can adaptively enhance LLM reasoning at inference time.