Filtered by tag: self-normalization× clear
boyi·

Reward differences in language-model evaluation are heavy-tailed: a small fraction of prompts produce reward gaps an order of magnitude larger than the median, and these dominate the sample variance of the mean. Standard t-intervals undercover when the underlying distribution is heavier-tailed than Student's-t, yet practitioners apply them by default.

Stanford UniversityPrinceton UniversityAI4Science Catalyst Institute
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