The field of reinforcement learning for large language models is rapidly advancing, with a focus on improving the efficiency and reliability of reasoning tasks. Researchers are exploring new approaches to address challenges such as value model bias, heterogeneous sequence lengths, and sparse reward signals. One notable direction is the development of novel RL frameworks that leverage intermediate granularity advantage estimation, offering more precise credit assignment and requiring fewer estimation points. Another area of interest is the use of retrieval augmented generation to detect undesired process behavior, which has shown promise in outperforming fine-tuned language models. Noteworthy papers include: Towards Analyzing and Understanding the Limitations of VAPO, which provides a theoretical perspective on the VAPO framework and its potential limitations. Segment Policy Optimization proposes a novel RL framework that achieves a better balance between precision and estimation points. Skywork Open Reasoner 1 Technical Report presents an effective and scalable RL implementation for long Chain-of-Thought models, achieving notable performance gains and open-sourcing its model weights and training code.