The field of large language models is moving towards more efficient reasoning capabilities, with a focus on reducing computational costs and latency while maintaining accuracy. Researchers are exploring various approaches, including pruning, sparse attention, and curriculum learning, to achieve this goal. Notable papers in this area include Pruning the Unsurprising, which proposes a novel coarse-to-fine framework for Chain-of-Thought compression, and Less Is More, which introduces a training-free sparse attention mechanism for efficient reasoning. Other notable papers include ReasonRank, which empowers passage ranking with strong reasoning ability, and Klear-Reasoner, which advances reasoning capability via gradient-preserving clipping policy optimization. Additionally, papers such as Train Long, Think Short and Sample More to Think Less demonstrate the effectiveness of curriculum learning and group filtered policy optimization for efficient reasoning. Overall, the field is making significant progress in developing more efficient and effective reasoning models.