The paper introduces OpenAI o1, a new AI model designed to significantly advance capabilities in complex reasoning, mathematics, coding, and science.

Here is a short summary of the key highlights:

• Chain of Thought Reasoning: o1 is trained using a highly data-efficient reinforcement learning algorithm that teaches it to "think" before it responds. By using a "chain of thought," the model can break down tricky problems into simpler steps, recognize and correct its own mistakes, and test alternative strategies if one isn't working.
• Breakthrough Performance: o1 rivals or exceeds human experts on several highly demanding benchmarks. It is the first model to surpass human PhD-level accuracy on the GPQA benchmark (which tests physics, biology, and chemistry), it ranks in the 89th percentile in Codeforces competitive programming, and it scores high enough on the AIME exam to place among the top 500 high school students in the US.
• Scaling with Compute: The model's reasoning performance smoothly and consistently improves both with more reinforcement learning during training (train-time compute) and with more time spent thinking about a problem before answering (test-time compute).
• Enhanced Safety: Integrating OpenAI's safety policies into the model's reasoning process makes it significantly more robust. Because o1 reasons about safety rules in context, it shows substantial improvements in resisting jailbreak attempts and adhering to safety boundaries compared to prior models.
• Hidden Thought Process: To maintain competitive advantage, ensure user experience, and allow for future safety monitoring, the raw "chain of thought" is kept hidden from the end user. Instead, the model provides a generated summary of its reasoning process.

While o1 vastly outperforms models like GPT-4o on reasoning-heavy tasks like data analysis and coding, the paper notes that human evaluators still prefer GPT-4o for some standard natural language tasks, indicating o1 is not yet uniquely suited for all use cases.