Unverified50% confidenceFactExact time
Training data can be divided into verifiable data (such as math problems, coding unit tests and STEM multiple-choice questions) and non-verifiable data (such as code style, copywriting expression and safety judgments).
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50%
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Long-term
Relevance
10/27/2026
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Related Claims
UnverifiedDatasets are typically divided into three parts: training set, validation set, and test set.78% similarVerifiedThe training data sources for specialized programming models include open-source code from GitHub, competitive programming solutions, technical documentation, and code review records, and the models are optimized for benchmarks such as HumanEval, MBPP, and SWE-bench.77% similarVerifiedNormalization parameters must be calculated only from the training set and then applied to the validation and test sets to avoid data leakage.76% similarUnverifiedPerforming integrity checks at the data entry point before training can reduce debugging difficulty, avoid wasting training runs due to data defects, and establish basic trust in the dataset75% similarUnverified测试用例应是可编辑、可检索的结构化数据，包含测试步骤、预期结果和变量定义，而非死板文档74% similar
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