Researchers propose SKIP, a self-knowledge-guided step-wise preference learning framework for concise reasoning in large language models. This framework adjusts the model's output style and uses a knowledge probing mechanism to guide the model to output an answer at each reasoning step, improving compression while minimizing performance degradation.