Rare Event Estimation via Iterative Unalignment
Research paper on estimating rare events from agent outputs, proposing a new importance sampling method with large efficiency gains.
This paper studies estimating the probability of rare events arising from agent stochastic outputs and develops a new importance sampling method for efficient computation, achieving large efficiency gains on rare events.
- Study of rare event estimation for stochastic agent outputs
- Proposes new IS method with differentiable language model proposals
- Achieves over 800x compute-weighted efficiency gains over Monte Carlo for rare events
- Vendors
- namkoong-lab
- Products
- iterative-unalignment
- Organizations
- namkoong-lab
- Countries
- United States
- AI models
- iterative-unalignmentQwen3-235B
From the original article
1 min read at arxiv.orgAs agents are deployed with increased autonomy, even extremely rare events along their stochastic output trajectories can occur and prove catastrophic. Safe deployment therefore does not depend on whether these events can occur, but on how often they might. We study the problem of estimating the probability of rare events that arise from stochastic variation in the agent's own actions.
By Hanming Yang · arxiv.org
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