ResearchOpenAI

OpenAI launches Control Room to stop unsafe workloads

Original title: Control Room: A central system to track and stop model workloads across our research infrastructure

Plain language

What it is: OpenAI introduced Control Room, a central system to track research workloads and quickly stop them if safety or security issues arise.
How it works: It monitors research jobs and can stop model output within 35 seconds of a decision to intervene. The system is designed to keep working even if other systems fail, and it stops all model activity if the Control Room itself goes down.
Where it is available: It is currently deployed across OpenAI's research infrastructure, specifically for frontier model research workloads.
Example: If a monitoring system detects a potential issue during a training job, it tells Control Room to stop the job, which immediately blocks the model from generating more output.

What it is: OpenAI launched Control Room, a centralized service designed to track and terminate model workloads across their research infrastructure.
How it works: Workloads request a job_id associated with a safety case, which is passed to the GPU-based inference engine during token sampling. The inference engine checks authorization with the Control Room service, which can deny the request and halt output within 35 seconds, failing closed if the service itself goes down.
Where it is available: It is deployed across OpenAI's research infrastructure, currently active for frontier model research workloads with a full rollout to remaining research workloads in progress.
Example: A monitoring model inspecting a reinforcement learning job can detect an anomaly and send a termination message with the job_id to Control Room, causing the inference engine to refuse subsequent token requests.

The headline and explanation on this page are based on OpenAI's post. The full details are in the original.

Control Room: A central system to track and stop model workloads across our research infrastructure