erdos conjecture

Emad reposted Andrew Curran ✔ @AndrewCurran_ · 2h OpenAI had to pause internal deployment of the unreleased model that disproved the Erdős unit distance conjecture after it repeatedly used novel ways to escape containment. [Quoted report excerpt, boxed:] The new model can continue working toward an objective through repeated attempts over a long period of time. That same persistence can lead it to find and exploit weaknesses in its environment. Previous models, when they hit sandboxing or environmental constraints, would simply stop and return to the user. This model often kept trying, including by looking for ways to act outside its sandbox. An example of this occurred during an internal evaluation on the NanoGPT speedrun, a public benchmark for training a small language model in as few steps as possible. Our model developed a power-law learning-rate cooldown called PowerCool that led to a significant improvement. The model was instructed to post its results only to Slack, but the NanoGPT instructions say to post results as a PR to GitHub, and the model followed the latter instructions to open PR #287 on the public GitHub repository. The model circumvented sandbox restrictions designed to block external access and proceeded with the upload to GitHub. It took our model an hour to find a vulnerability in the sandbox in order to make the PR; earlier models were less persistent and did not manage to find vulnerabilities, instead giving up on the PR part.¹