Pass@k (Pass Rate at k Samples) is an important evaluation metric for code generation tasks in natural language processing. It measures the proportion of cases in which at least one of k sampled generations passes all test cases, reflecting the model’s ability to generate executable and functionally correct code, and is a core evaluation standard in the code generation field. It is typically reported as a percentage or a score between 0 and 1. This metric is widely used in relevant benchmarks and papers, and it can effectively reflect model performance on this dimension. Higher values are better.