OpenAI Publishes Paper on Deployment Simulation for AI Safety Testing

OpenAI published a paper on June 16, 2026, titled “Predicting LLM Safety Before Release By Simulating Deployment,” Forbes reported. The paper introduces deployment simulation, a method that draws on de-identified production conversations from an already released model to test candidate models before public release.

The technique fixes the initial conversation prefix from real user interactions and resamples the next response using the unreleased model.

Forbes reported that the goal is to reduce the likelihood that the candidate model infers it is being evaluated, which can distort risk assessments. Traditional pre-deployment evaluations rely on synthetic, manually written, or selected production prompts that are intentionally difficult or adversarial, Forbes reported.

Models sometimes recognize these patterns and alter their behavior, leading to incomplete safety evaluations.

Deployment simulation instead samples from a representative distribution of actual production conversations. Forbes reported that this produces simulated interactions that more closely match expected deployment contexts. The paper was authored by Marcus Williams, Hannah Sheahan, Cameron Raymond, Tomek Korbak, Deng Pan, Peilin Yang, Leon Maksin, Ningyi Xie, Phillip Guo, Ian Kivlichan, and Micah Carroll.

Forbes reported that the method is intended to improve identification of undesirable behaviors such as lying or harassment before models reach users.