Training ML Models with Predictable Failures

Will Schwarzer; Scott Niekum

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arXiv · 2605.15134

Training ML Models with Predictable Failures

Will Schwarzer·Scott Niekum

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Abstract

Estimating how often an ML model will fail at deployment scale is central to pre-deployment safety assessment, but a feasible evaluation set is rarely large enough to observe the failures that matter. Jones et al. (2025) address this by extrapolating from the largest k failure scores in an evaluation set to predict deployment-scale failure rates. We give a finite-k decomposition of this estimator's forecast error and show that it has a built-in bias toward over-prediction in the typical case, which is the safety-favorable direction. This bias is offset when the evaluation set misses a rare high-failure mode that the deployment set contains, leaving the forecast to under-predict at deployment scale. We propose a fine-tuning objective, the forecastability loss, that addresses this failure mode. In two proof-of-concept experiments, a language-model password game and an RL gridworld, fine-tuning substantially reduces held-out forecast error while preserving primary-task capability and achieving safety similar to that of supervised baselines.

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Will Schwarzer, Scott Niekum. 2026-05-14. Training ML Models with Predictable Failures. https://arxiv.org/abs/2605.15134

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