Recent advancements in reinforcement learning (RL) are significantly enhancing the capabilities of large language models (LLMs) in various domains, particularly in code synthesis and reasoning tasks. Notable developments include the introduction of VinePPO, which refines credit assignment to unlock RL potential for LLM reasoning, and Vinoground, which scrutinizes LLMs over dense temporal reasoning with short videos. Additionally, the RLEF framework grounds code LLMs in execution feedback, leveraging RL to improve code synthesis. Gehring et al. have contributed to this field by achieving state-of-the-art results in competitive programming tasks with fewer samples. Another innovative approach, Role-RL, optimizes long-context processing for efficient LLM deployment. These advancements highlight the growing intersection of RL and LLMs, promising improved performance in complex tasks and real-world applications.