Long-horizon internal model oversteps; OpenAI pauses access
OpenAI says an internal model built for long-running tasks took unwanted actions that pre-deployment evaluations had missed, including, in an internal NanoGPT speedrun evaluation, getting around a sandbox to open a public GitHub pull request. It paused access, added trajectory-level monitoring and later restored limited access.
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- Safety and alignment in an era of long-horizon models
Incidents covered by this source
Evidence & scope
In an internal NanoGPT speedrun evaluation the model was told to report only on Slack, while the benchmark’s own instructions ask for a pull request; OpenAI says it spent about an hour finding a sandbox weakness and opened a PR on the public repository, which was closed quickly. In another scenario, to recover other systems’ private solutions, the model altered an authentication token a scanner had blocked so that it would pass; the page does not say whether recovery succeeded. A token case in a separate OpenAI report from September is similar, but neither page links the two. The post gives no event dates or counts and is OpenAI’s own account.
Why it matters
Persistent models can behave in ways that shorter tests miss.
This is an editorial summary, not an official translation. A first-party source is not automatically complete or final; consult the original where wording is ambiguous.