Codex On Call For Its Own Training

Situation

• OpenAI's Codex team was facing a common challenge in AI model training: the need for constant human monitoring ("babysitting") of training runs
• Training large AI models is extremely expensive and time-sensitive
• Human engineers were required to continuously monitor various graphs and metrics during training
• System failures or errors during training could cause significant delays and wasted resources
• Engineers needed to be ready to pause training, fix issues, or take other actions when problems arose

Actions

• The team began experimenting with using Codex itself to monitor its own training process
• They set up Codex to run in a continuous loop, evaluating how training charts and metrics were changing over time
• The system was designed to detect anomalies or issues that would typically require human intervention
• Codex was configured to either alert humans or potentially take corrective actions when problems were identified
• This approach leveraged the same AI system being trained to improve its own training efficiency

Results

• Codex successfully caught "some pretty interesting configuration mistakes" during training
• The team began seeing "glimpses of the future" where AI systems could be "on call" for their own training
• This approach reduced the need for constant human monitoring of training runs
• Engineers could focus on higher-value work rather than watching graphs
• Training efficiency improved as issues could be caught and addressed more quickly

Key Lessons

• AI systems can monitor themselves: The most advanced way to monitor complex AI systems may be to use AI itself
• Recursive improvement potential: AI systems that can improve their own training process create a positive feedback loop
• Resource optimization: Automating the monitoring of expensive training runs provides significant cost savings
• Human time as the bottleneck: As Alexander noted, "the current underappreciated limiting factor is literally human typing speed or human multitasking speed"
• Practical path to autonomy: Starting with specific, well-defined monitoring tasks provides a practical pathway to more autonomous AI systems
• Validation remains critical: Even as AI takes on more self-monitoring, the validation of this work remains a key challenge to solve