The field of large language models (LLMs) is moving towards improving reasoning capabilities and efficient distillation methods. Researchers are exploring new frameworks and techniques to enhance the performance of LLMs in mathematical reasoning, code generation, and other tasks. A key focus area is the development of data-efficient distillation methods that can optimize the Pareto frontier of reasoning distillation, allowing for better performance with fewer resources. Another important direction is the investigation of robustness and safety in LLMs, including the identification of membership and memorization privacy risks and the development of safe distillation methods. Noteworthy papers in this area include: Less is More: Selective Reflection for Compatible and Efficient Knowledge Distillation in Large Language Models, which proposes a novel data curation framework for improving distillation outcomes. Putnam-AXIOM: A Functional and Static Benchmark, which introduces a new benchmark for evaluating the mathematical reasoning capabilities of LLMs. Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning, which presents a data-efficient distillation framework that optimizes the Pareto frontier of reasoning distillation. Decoupling Understanding from Reasoning via Problem Space Mapping for Small-scale Model Reasoning, which proposes a new framework that decouples understanding from reasoning by mapping natural language problems into a canonical problem space.