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.
1
Sources
50%
Confidence
Long-term
Relevance
9/17/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe capability of supervised learning models is bounded by the ceiling of data quality and cannot surpass the level of the teacher algorithm.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
Cite This Claim
Stable URI
https://kongchang.com/claim/929637API
curl https://kongchang.com/api/v1/knowledge/claims/929637MCP
get_claim(id=929637)