OpenAI has published initial guidelines advocating for structured "safety cases"—comprehensive, evidence-based risk arguments—before continuing any frontier reinforcement learning training run. The organization aims to codify these practices as an aspirational standard to manage the emergent complexity of advanced AI models.
The proposed framework requires safety cases to cover three technical aspects: alignment training, containment, and monitoring. Key measures include automated and manual dataset reviews, grader tuning, offline alignment evaluations, and backtesting to prevent reward hacks and misalignment. Containment strategies involve hardening sandbox infrastructure, conducting containment red-teaming with frontier checkpoints, limiting cross-sample communication, and using immutable transcripts for incident investigation.
Operational best practices include requiring pre-mortem dissents, senior leadership approvals with veto power, and defined accountability for training runs. The guidelines also outline protocols for investigating severe misalignment incidents, such as root-cause analysis, public disclosures, and creating regression tests to prevent future similar behaviors.
OpenAI is currently implementing these recommendations internally and expects the practices to evolve as they iterate on their internal safety processes.