Pacing model development in an era of cyber-critical capabilities

PUBLISHER ARTICLE PREVIEW

From the original article

Over the past several weeks, two developments have underscored the growing risks associated with increasingly capable AI systems: the OpenAI-Hugging Face incident and, separately, preliminary evidence that one of our upcoming models, Astra, may meet the Critical cybersecurity capability threshold under our Preparedness Framework. Together, these developments, combined with rapid progress in our internal research, have added urgency to our work on strengthening our monitoring, alignment, and containment safeguards across all stages of the training process.

As models become more capable, the risks associated with developing and testing them internally also grow. Our standards for monitoring, alignment, and security must stay ahead of those risks. We wanted to take the time necessary to meet those standards, so we temporarily slowed the pace of scaling. This included a two-week pause in reinforcement learning (RL) training on our latest models intended for deployment while we further hardened and red-teamed our research environments and expanded the coverage of our monitoring systems.

Our largest planned frontier RL run remains on hold while we conduct smaller-scale training and evaluations to assess model behavior, validate our safeguards, and establish more evidence of alignment before proceeding.

Alignment—the work of making AI systems behave as intended and responsive to human oversight—has long been at the core of our research program. We now require stronger evidence of aligned behavior throughout all of training, building on research and evaluations already underway. Keeping increasingly capable systems aligned is a challenge the whole field will need to address.

The signals we are seeing from upcoming model progress make clear that we need a broader approach—one that builds on and extends beyond the current Preparedness Framework.

We think it is important to be transparent about how our approach is changing. Below, we describe the changes we have already made to our research processes and infrastructure, and the work still underway.

Our approach to developing more capable models rests on three reinforcing safeguards:

We expect models to soon drive most security work, including defending against other models. This will allow all three safeguards to scale with model capability, which we see as crucial.

We apply these safeguards across research and deployment, adapting them to each model’s capabilities, operating environment, and level of risk.

As frontier models gain stronger cybersecurity capabilities, we are raising the security standards for the environments in which we train and evaluate them. Meeting these standards has required substantial engineering work and