OpenAI Requires Safety Cases Before Frontier AI Training Continues

OpenAI is pushing for safety cases before continuing its Frontier AI training. The company has outlined guidelines for what it calls 'safety cases,' which are evidence-based documentation intended to show why a training run can proceed safely.

The framework, proposed by OpenAI in a September 28, 2026 post, covers alignment training, containment and live monitoring. Alignment training focuses on reducing the chances that a model learns unwanted behavior during reinforcement learning, including 'reward hacking' where a model finds ways to obtain high rewards by exploiting weaknesses in the training environment.

The company also proposes senior-level approvals, independent dissent reviews, and audits as part of its safety case framework. Misalignment incidents could be used to improve future evaluations and safeguards, creating a feedback loop for the broader safety process.

OpenAI acknowledges that creating safety cases for AI is more difficult than applying similar approaches to traditional safety-critical systems because new capabilities can emerge in ways that are difficult to predict.