Rare Event Estimation via Iterative Unalignment

TL;DR - This paper introduces an importance-sampling method that estimates extremely rare failures in stochastic agent trajectories by iteratively perturbing a language model’s weights. It makes quantifying catastrophic events far more computationally practical than naive Monte Carlo sampling.

• Uses a differentiably parameterized language model as the importance-sampling proposal, enabling gradient-based optimization in weight space.
• Combines a differentiable event-amplification surrogate with adaptive regularization to balance rare-event discovery against estimator stability.
• Evaluated on models of roughly 120M and 2.6B parameters across more than 300 events, including probabilities as low as (10^{-9}).
• In the most verifiable settings, achieves over (800\times) compute-weighted efficiency gains for events rarer than (10^{-7}).