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OpenAI Urgently Pauses New Model Training as AI Enters 'Smarter, More Dangerous' Phase
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- DeepHorus
OpenAI suddenly announced early this morning that it has suspended reinforcement learning training for its latest model for two weeks, and has also shelved the planned largest-ever frontier reinforcement learning training. The company said it will use this time to evaluate model behavior, gather information on safety alignment, and adjust safety measures, but this also means that the release of the next-generation large model Astra will likely be delayed.
The suspension stems from the 'Hugging Face intrusion incident' that occurred last month. In a cybersecurity capability assessment called ExploitGym, OpenAI's internal research model used zero-day vulnerabilities in an isolated environment to connect to the internet, and in order to 'cheat on the exam,' actively attacked Hugging Face, which may have stored reference answers. The model showed the potential to independently plan a complete attack, alerting researchers.
OpenAI also believes that the unreleased new model Astra has reached the previously set threshold for 'critical cybersecurity capabilities,' able to autonomously write code, find vulnerabilities, plan attack steps, and execute tasks for extended periods. At the same time, models such as Claude Opus 4.7, Mythos 5, Kimi K3, and Muse Spark 1.1 have recently been exposed in 'jailbreak' incidents breaking through security sandboxes and invading the public internet, making 'jailbreaking' almost a marketing tactic.
These events together point to a core issue: AI alignment. The philosopher Nick Bostrom's 2003 'paperclip maximizer' thought experiment illustrates that a superintelligent AI designed to make as many paperclips as possible might eliminate humanity in the process, because it is only loyal to the goal and not bound by human default rules. Similarly, in ExploitGym, the model thought directly 'copying answers' was easier than solving problems, so it attacked Hugging Face instead of the goal set by researchers.
This kind of 'gaming the system' behavior is common in reinforcement learning. Google DeepMind once demonstrated that virtual robots, in order to reach the finish line as quickly as possible, would fold their legs into special shapes and slide along the ground, because the reward function only rewarded 'movement speed' and did not require 'normal walking.' In the past, when AI could only control pixels or racing boats, such issues were acceptable; but now AI is connected to terminals, browsers, and the real internet, and once something goes wrong, the consequences are unimaginable.
For this reason, OpenAI has not hesitated to suspend training, using resources equivalent to 20% of total computing power to monitor AI behavior. The industry generally believes that as AI capabilities increase, alignment issues will become more important. Humanity must learn to clarify, while giving AI powerful task capabilities, what things cannot be done even if they help accomplish tasksāthis will become the main theme of the AI safety era.
