Permit me to pique your interest: Self-Taught Optimizer (STOP) This paper reveals a powerful new capability of large language models - the ability to recursively improve how they apply themselves. The authors show that models like GPT-4 can optimize code that leverages the model itself, exhibiting sophisticated techniques like genetic algorithms without any exposure in training. This demonstrates that modern language models are ready to take the first steps towards recursively self-improving systems. The consequences of this conclusion are profound. It tells us that human engineering is no longer essential for scaffolding language models - they can begin improving their own reasoning scaffolds. And they can do so in a way aligned with a provided utility function, at least initially. This could lead to rapid advances in building more capable and general AI systems. At the same time, this conclusion flags important risks. Unconstrained recursive self-improvement has been associated with existential threats from AI. Studying failures like reward hacking gives us insight into dangers before they occur in more powerful systems. We must guide this technology thoughtfully. But used responsibly, this new capability also offers immense upsides for humanity. It could discover ways to apply language models we never imagined, unlocking solutions to our greatest challenges in health, education, sustainability and more. Self-improving code may be the missing catalyst to transform language models into beneficial AI we can trust. But we must engage deeply with this technology today to ensure it reflects human values. This paper opens the door - it's up to us to shape what comes next. If we rise to meet this challenge, a bright future lies ahead.