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.
HCAST: Human-calibrated autonomy software tasks, 2025
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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.