The field of large language models (LLMs) is moving towards more effective ensemble methods and improved reasoning capabilities. Researchers are exploring innovative approaches to leverage diversity in LLMs, including model diversity and question interpretation diversity, to enhance performance. Additionally, there is a growing focus on developing more advanced reward models that can improve the reasoning abilities of LLMs in complex scenarios. Reinforcement learning is being applied to multimodal reasoning tasks, and novel frameworks are being proposed to address the limitations of existing models. Noteworthy papers include: LENS, which proposes a novel approach to learning ensemble confidence from neural states for multi-LLM answer integration, outperforming traditional ensemble methods. Libra, which introduces a comprehensive framework for evaluating and improving the performance of reward models in complex reasoning scenarios, achieving state-of-the-art results on various benchmarks.