PathCal introduces a new method for enhancing the efficiency of Large Reasoning Language Models by distinguishing between different types of reflection markers during reasoning tasks. This approach improves accuracy while reducing the length of generated outputs, potentially setting a new standard for model performance in complex reasoning scenarios.