A finite-k decomposition reveals a bias toward over-prediction in failure rate extrapolation from evaluation data, addressed by a new forecastability loss that improves held-out forecast accuracy in language-model and RL experiments.
Black-box rare-event simulation for safety test- ing of AI agents: An overview.Journal of the Oper- ations Research Society of China, 13:750–774
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Training ML Models with Predictable Failures
A finite-k decomposition reveals a bias toward over-prediction in failure rate extrapolation from evaluation data, addressed by a new forecastability loss that improves held-out forecast accuracy in language-model and RL experiments.