Is This GPT-5? OpenAI o1 Full Breakdown
TLDROpenAI has unveiled a new model series named 'o1', marking a departure from the GPT naming convention. The series includes an 'o1 preview' model, which, despite being slower with a response time of 20-30 seconds, offers a significant performance boost, rivaling PhD students in complex subjects. It excels in logical and reasoning tasks, with a notable 70% increase in problem-solving accuracy compared to GPT-4. The 'o1 mini' model is a more affordable alternative. Both models feature a 128k context window, and while they show impressive reasoning capabilities, they do not demonstrate an all-around performance increase, indicating that AGI is not yet achieved. The key innovation is the integration of 'chain of thought' with reinforcement learning, which enhances the model's consistency and reasoning without deviating from the prompt. The model's private chain of thought process is not disclosed, and access is limited to paid users with a cap on queries. This development suggests a new approach to scaling AI models, focusing on inference time rather than just pre- and post-training.
Takeaways
- 🆕 OpenAI has introduced a new model series called 'o1', moving away from the 'GPT' naming convention.
- 📈 The 'o1' series includes an 'o1 preview' model and an 'o1 Mini' model, with the former being 3-4 times more expensive than GPT-4.
- 🔍 Both models have a 128k context window, but the 'o1 preview' is slower, taking 20-30 seconds to generate an answer.
- 🎓 The 'o1 preview' model shows significant performance in reasoning tasks, rivaling PhD students in certain benchmarks.
- 📊 In the International Mathematics Olympiad qualifying exam, 'o1' solved 83% of problems, a 70% increase from GPT-4's 13%.
- 🧠 The model excels in logical and reasoning tasks, with a notable jump in accuracy in the MML Ed College mathematics and formal logic categories.
- 🤖 The main breakthrough is the integration of 'chain of thought' on top of reinforcement learning, which enhances the model's consistency and performance.
- 🚫 The 'o1' model's private chain of thought process is not shared, and the model is currently limited to paid users with a 30-message weekly limit.
- 🔑 The model's potential is seen in its ability to scale AI performance through longer inference times, suggesting a new dimension for AI model development.
- ❓ There are concerns about over-optimistic evaluation and the need for caution when interpreting the benchmarks, as the full 'o1' model is not yet available for public testing.
Q & A
What is the new model series announced by OpenAI?
-OpenAI has announced a new model series called 'o1', which includes an 'o1 preview' model and an 'o1 Mini' model.
What are the key differences between the 'o1 preview' and 'o1 Mini' models?
-Both models have a 128k context window. The 'o1 preview' model is 3 to 4 times more expensive than GPT-4 and is much slower, taking around 20 to 30 seconds to generate an answer, but offers a significant performance increase. The 'o1 Mini' model is slightly cheaper.
How does the 'o1 preview' model perform in terms of problem-solving compared to GPT-4?
-The 'o1 preview' model has an impressive performance increase, solving 83% of the problems in the qualifying exam for the International Mathematics Olympiad, compared to GPT-4's 13%.
What is the 'Chain of Thought' and how does it contribute to the model's performance?
-The 'Chain of Thought' is a method where the model thinks about what it has generated, allowing it to plan, reflect, and improve its results. This is combined with reinforcement learning to teach the model how to think properly, which is baked into the training process.
How does the 'o1' model handle reasoning tasks compared to other benchmarks?
-The 'o1' model excels at reasoning and logical tasks, with significant performance jumps in categories like MML Ed College Mathematics and Formal Logic. However, it does not show improvements in every aspect, such as the English Literature category.
What is the significance of the 'o1' model's ability to think for longer periods during inference?
-The 'o1' model's ability to think for longer during inference suggests a new dimension for scaling AI models, where compute spent on inference could be as valuable as pre-training and post-training, potentially leading to better performance in reasoning tasks.
How does the 'o1' model's approach to data synthesis and training techniques differ from previous models?
-The 'o1' model has refined its data synthesizing skills and training techniques, allowing it to achieve scores beyond any agent frameworks or other frontier models. Its Chain of Thought is more deeply integrated, and it can handle high-quality synthetic data sets without collapsing.
What limitations does the 'o1' model have in terms of access and usage?
-The 'o1' model is limited to paid users and allows only 30 messages per week per user. It is also currently unreleased, so its full capabilities and performance are still under evaluation.
What are the future plans for the 'o1' model according to OpenAI researchers?
-OpenAI researchers aim for future versions of the 'o1' model to think for hours, days, or even weeks to see if this scaling in inference time would improve performance.
How can interested individuals access more information and demos about the 'o1' model?
-Individuals can access demos and more information about the 'o1' model through links provided in the description of the video or by following the creator on social media platforms like Twitter.
Outlines
🤖 OpenAI's New AI Model Series: 01
OpenAI has introduced a new AI model series named '01,' which replaces the previous GPT naming convention. The series includes two models: the 01 preview and the 01 Mini. Both have a 128k context window, with the 01 preview being more expensive and slower to generate responses but offering significant performance improvements, particularly in logical and reasoning tasks. It achieves an impressive 83% accuracy on the International Mathematics Olympiad qualifying exam, a stark contrast to GPT-40's 13%. The 01 Mini is positioned as a more affordable option. The models are designed with a 'chain of thought' approach integrated into reinforcement learning, which enhances their consistency and reasoning capabilities. However, the performance gains are not universal, with little improvement seen in English literature benchmarks. The new models are currently accessible only to paid users with a limit on the number of queries.
🔍 Evaluating OpenAI's 01 Model and Future Implications
The video discusses the potential of OpenAI's 01 model, suggesting that it might be over-optimized for benchmarks, hence the impressive performance should be taken with caution. The presenter plans to provide a deeper analysis of the model's architecture and performance in the future. They also mention the possibility of 'inference time scaling,' where allowing the model to think for longer periods could improve its reasoning capabilities. This approach challenges traditional AI scaling methods that focus on pre- and post-training. The presenter encourages viewers to stay updated for more detailed insights and to explore their newsletter for additional information on AI advancements. The video concludes with acknowledgments to supporters and a prompt to follow the presenter on social media for further updates.
Mindmap
Keywords
💡GPT-5
💡01 Model Series
💡Context Window
💡Reinforcement Learning
💡Chain of Thought
💡Benchmarks
💡International Mathematics Olympiad
💡AGI
💡Private Chain of Thought
💡Inference Time Scaling
Highlights
OpenAI has announced a new model series called 01, moving away from the GPT naming convention.
The 01 series includes an 01 preview model and an 01 Mini model, both with a 128k context window.
The 01 preview model is 3 to 4 times more expensive than GPT-4 and has a slower response time of 20 to 30 seconds.
Despite the slower speed, the 01 preview model shows significant performance improvements, rivaling PhD students in certain subjects.
The 01 model achieved an 83% success rate on the International Mathematics Olympiad qualifying exam, a 70% increase from GPT-4's 13%.
The 01 preview model scored around 56% on the same exam, marking a 43% accuracy increase from GPT-4.
In the MML Ed College mathematics category, the 01 model's performance jumped from 75.2% to 98%.
The formal logic category saw a similar jump from 80% to 97%.
The model's focus is on reasoning and solving complex logical tasks, not general improvements across all areas.
The main breakthrough is the integration of a 'chain of thought' mechanism with reinforcement learning.
The model is trained to think about its generated content, planning, reflecting, and improving results before presenting them.
The 'chain of thought' process is private, and users only see the summary and time taken for thinking.
Rumors suggest that each query generates up to 100K tokens for the private chain of thought.
The 01 model is limited to paid users with a cap of 30 messages per week.
The model's longer thinking time correlates with better performance on reasoning tasks, indicating a new dimension for AI model scaling.
OpenAI researchers aim for future models to think for extended periods to see if performance scales effectively.
There is skepticism about the model's potential for overfitting and the need to take benchmark results with caution.
The 01 model's success is attributed to refined data synthesis and training techniques, not just fine-tuning.
The model's potential and practical applications are still under evaluation, and more information is needed for a complete assessment.