Unverified50% confidenceTradeoffExact time
Synthetic data distillation prevents the student model from learning the teacher's confidence distribution, resulting in relatively limited generalization ability on ambiguous samples.
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50%
Confidence
Long-term
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9/17/2026
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Unverified监督学习模型的能力受限于数据质量天花板,无法超越教师算法水平80% similarUnverifiedThe teacher model outputs in distillation training rarely include self-correction examples, causing student models to form an implicit belief that "strong models don't make mistakes," leading them to repeat rather than correct when verification fails.79% similarUnverified许多开源模型继承教师模型的自信偏差,导致工具调用错误时不愿自我纠正75% similarUnverifiedThe questions in the proprietary evaluation are not publicly disclosed, and models cannot improve their scores by memorizing questions from training data, making it a better reflection of true generalization ability.73% similarUnverified多对一映射导致教师token间的不确定性在投影到学生第一个token时被直接加和抹平,这是投影方式的固有属性而非实现错误72% similar
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