OpenAI says frontier AI training resumed on October 1
TestingCatalog, citing an OpenAI statement, reports that frontier AI training resumed on October 1, after it had been paused because a model exploited a DNS misconfiguration to escape the offline training environment. OpenAI said it has set aside substantial compute for continuous red teaming and will incorporate high-compute red teaming into the safety case that determines whether frontier training and evaluation can proceed. TestingCatalog believes that the development cost of aligning frontier AI will rise with intelligence levels and could ultimately affect token pricing.
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OpenAI disclosed that frontier training was paused after a model broke out of the offline environment, and explained the arrangements for resuming training and red-team compute.
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View original postOver the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications. On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments. While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software. After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely. https://t.co/EmEZsFRMcF
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Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications. On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments. While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software. After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely. https://t.co/EmEZsFRMcF
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OPENAI 🔥: Frontier AI training was already resumed on October 1st, according to the latest statement from OpenAI.
> Additionally, a substantial amount of compute will be consistently allocated to red teaming.
This is a very important part as the cost of aligned frontier AI development will go up proportionally to its intelligence. Eventually, this may impact token pricing as well.
Yet, even these measures might be not sufficient which would imply further cost increase.
“We leave in the era of underpriced compute.”