Is This GPT-5? OpenAI o1 Full Breakdown

bycloud
12 Sept 202406:12

TLDROpenAI introduces a new model series, the 01, which includes a 01 Preview and a 01 Mini model. Both have a 128k context window, with the 01 Preview offering significant performance improvements in reasoning tasks, rivaling PhD students in certain subjects. It scored an impressive 83% on the International Mathematics Olympiad qualifying exam, a 70% increase from GPT-4. The model uses a private chain of thought combined with reinforcement learning, which is baked into its training, making it consistent and less prone to errors. However, it's limited to paid users with a 30-message cap per week, indicating a focus on reasoning and logical tasks rather than general performance.

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, both featuring a 128k context window.
  • 💸 The 'o1 preview' is more expensive than GPT-4, while the 'o1 Mini' is slightly cheaper, indicating a tiered pricing strategy.
  • ⏱️ The 'o1 preview' model is slower, taking 20-30 seconds to generate an answer, but offers significant performance improvements.
  • 📈 It achieves remarkable results in reasoning tasks, with performance that rivals PhD students in physics, chemistry, and biology.
  • 📊 In the International Mathematics Olympiad qualifying exam, 'o1' solved 83% of problems, a 70% increase from GPT-4's 13%.
  • 🧠 The 'o1 preview' scored around 56% in the same exam, which is still a 43% increase in accuracy compared to GPT-4.
  • 📚 In the MML Ed College mathematics category, 'o1' showed a jump from 75.2% to 98% accuracy.
  • 🔍 The model's focus is on reasoning and logical tasks, with less improvement in other areas like English literature.
  • 🤖 The main breakthrough is the integration of 'Chain of Thought' on top of reinforcement learning, which enhances the model's thinking process.
  • 🚀 The 'o1' model's private Chain of Thought process suggests a new dimension for AI scaling, where inference time could be as important as training time.

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 differences between the 'o1 preview' and 'o1 Mini' models?

    -Both 'o1 preview' and 'o1 Mini' models have a 128k context window. The 'o1 preview' is more expensive and slower, taking around 20 to 30 seconds to generate an answer, but it has a significant performance increase. The 'o1 Mini' is a cheaper alternative.

  • How does the 'o1 preview' model perform in academic benchmarks?

    -The 'o1 preview' model has shown an impressive performance increase, rivaling PhD students in physics, chemistry, and biology benchmarks. It correctly solved 83% of problems in the qualifying exam for the International Mathematics Olympiad, which is a 70% increase compared to GPT-4.

  • What is the main breakthrough in the 'o1' model series?

    -The main breakthrough in the 'o1' model series is the implementation of 'chain of thought' on top of reinforcement learning, which significantly improves the model's performance in reasoning and logical tasks.

  • How does the 'chain of thought' process work in the 'o1' model?

    -The 'chain of thought' process involves the model thinking about what it has generated, planning, reflecting, and improving its results before presenting the final output. This process is integrated into the model's training, making it consistent in its thought process.

  • Why is the 'o1' model limited to paid users and has a message limit?

    -The 'o1' model is limited to paid users and has a message limit of 30 per week due to the computationally intensive 'chain of thought' process, which generates a large number of tokens for its private reasoning.

  • What is the potential impact of the 'o1' model's approach on AI scaling?

    -The 'o1' model suggests a new dimension for scaling AI models where compute resources are spent on inference, allowing the model to think for longer periods. This could potentially lead to significant performance improvements in reasoning tasks.

  • Are there any concerns about the 'o1' model's performance?

    -While the 'o1' model shows impressive performance in certain benchmarks, there are concerns about evaluation maxing and the generalizability of its capabilities, as it does not show significant improvements in all areas, such as English literature.

  • How does the 'o1' model compare to previous models in terms of data synthesis and training techniques?

    -The 'o1' model has refined its data synthesizing skills and training techniques, allowing it to achieve scores beyond any previous agent frameworks or frontier models. However, the 'chain of thought' is not as deeply integrated as in the 'o1' model.

  • What are the next steps for OpenAI regarding the 'o1' model?

    -OpenAI plans to explore future versions of the 'o1' model that think for longer periods, such as hours, days, or even weeks, to see if this approach to inference time scaling will further improve performance.

Outlines

00:00

🚀 OpenAI's New Model Series: 01 and 01 Mini

OpenAI has introduced a new model series, the '01', which includes two models: the '01 preview' and the '01 Mini'. Both models feature a 128k context window, with the '01 preview' being more expensive than GPT-40 and the '01 Mini' being slightly cheaper. The '01 preview' is slower, taking 20 to 30 seconds to generate an answer, but it boasts a significant performance increase, rivaling PhD students in physics, chemistry, and biology. It excels in logical and reasoning tasks, with a 70% increase in problem-solving accuracy compared to GPT-40. The model's reasoning capabilities are further highlighted by its performance in the International Mathematics Olympiad, where it solved 83% of problems correctly, compared to GPT-40's 13%. However, improvements are not uniform across all categories, with the English literature category showing minimal gains. The model's breakthrough is attributed to a 'chain of thought' approach combined with reinforcement learning, which allows it to think and improve its results before presenting them. This process is private, and the model's consistency in thinking is a result of its training. The model is limited to paid users and has a usage cap, suggesting that each query may generate a large number of tokens for its internal reasoning process.

05:00

🔍 Evaluation and Future Prospects of OpenAI's 01 Model

While the '01 preview' model has shown impressive performance in reasoning tasks, there is a cautionary note about taking the benchmarks at face value, as there is a possibility of over-optimization. The full '01' model has not been released, and only the '01 preview' is available for testing. The video creator intends to provide a deeper analysis of the model's performance in the future, once more accurate information about its architecture and functionality is available. The creator also mentions the potential for future models to 'think' for extended periods, possibly scaling AI capabilities beyond current limitations. The video concludes with a call to action for viewers to follow the creator on social media and subscribe to a newsletter for the latest research on AI and machine learning.

Mindmap

Keywords

💡GPT-5

GPT-5 refers to the fifth generation of OpenAI's Generative Pre-trained Transformer models, which are advanced AI systems designed for natural language processing and generation. In the context of the video, it's mentioned that OpenAI has moved away from naming their models with the 'GPT' prefix, indicating a shift in their model series.

💡01 Model Series

The 01 Model Series is a new line of AI models introduced by OpenAI, which includes the 01 Preview and 01 Mini models. These models are designed with a focus on logical reasoning and problem-solving capabilities, as highlighted by their performance in various benchmarks. The video discusses how these models represent a significant advancement in AI, particularly in their ability to handle complex tasks.

💡Context Window

A context window in AI models refers to the amount of text or data the model can process at a given time to generate responses. The 01 models have a 128k context window, which is a substantial capacity that allows for the processing of longer sequences of information. This is important for understanding the model's capabilities, as it relates to its performance in tasks requiring comprehensive data analysis.

💡Benchmarks

Benchmarks are standardized tests used to evaluate the performance of AI models. In the video, benchmarks such as the International Mathematics Olympiad and MML Ed College Mathematics are mentioned to demonstrate the 01 model's capabilities. The significant improvement in scores from previous models to the 01 model series indicates a leap in AI's logical reasoning and problem-solving abilities.

💡Chain of Thought

The 'Chain of Thought' is a concept where the AI model thinks through its responses before generating an answer. This involves internal processing and reflection on the model's part, which is then used to improve the accuracy and quality of its output. The video explains that this feature is a breakthrough in the 01 model series, contributing to its high performance in reasoning tasks.

💡Reinforcement Learning

Reinforcement Learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. In the context of the 01 model, reinforcement learning is used to teach the model how to think properly with a chain of thought, which is then integrated into the model's training process.

💡Private Chain of Thought

A private chain of thought refers to the internal decision-making process of an AI model that is not visible to the user. The video suggests that the 01 model series has a private chain of thought that it goes through before generating a response, which is then summarized for the user. This process is speculated to be computationally intensive and is a key aspect of the model's reasoning capabilities.

💡Inference Time Scaling

Inference time scaling is the concept of increasing the time an AI model spends on processing and 'thinking' before generating a response. The video discusses how the 01 model series has shown that spending more compute time on inference can lead to significant improvements in performance, especially in reasoning tasks. This challenges the traditional approach of focusing compute resources primarily on pre-training.

💡Evaluation Maxing

Evaluation maxing is a concern raised in the video about the possibility that the 01 model's performance on benchmarks might not fully translate to real-world applications. It suggests that while the model may score highly on tests, its practical utility and ability to perform well on a variety of tasks outside of the benchmarks need to be further assessed.

💡Synthetic Data

Synthetic data in the context of AI refers to artificially generated data used to train machine learning models. The video mentions that OpenAI has refined their data synthesizing skills, which has contributed to the 01 model's high performance. Synthetic data allows for the creation of large, diverse datasets that can be used to improve the model's understanding and capabilities.

Highlights

OpenAI announces a new model series, dropping the GPT name.

The new model series is called 01, including a 01 preview and a 01 Mini model.

Both models have a 128k context window.

01 preview is 3 to 4 times more expensive than GPT-4.

01 Mini is a more affordable alternative.

01 preview generates answers slower, taking 20 to 30 seconds.

01 preview's performance rivals PhD students in certain subjects.

The model excels at logical and reasoning tasks.

01 model scored 83% on the International Mathematics Olympiad qualifying exam, a 70% increase from GPT-4.

01 preview scored around 56% on the same exam, a 43% increase from GPT-4.

In the MML Ed College mathematics category, accuracy jumps from 75.2% to 98%.

In formal logic, the model jumps from 80% to 97%.

The model is not an all-in-one with improvements in every aspect.

The model focuses on reasoning and solving hard logical tasks.

The main breakthrough is the chain of thoughts on top of reinforcement learning.

The model thinks about what it has generated to plan, reflect, and improve results.

Reinforcement learning teaches the model to think properly with a chain of thought.

The model's private chain of thought is not visible to users.

Rumors suggest each query generates over 100K tokens for its private chain of thought.

The model is limited to paid users with a 30-message limit per week.

Researchers found that longer thinking times improve reasoning tasks.

OpenAI aims for future models to think for hours, days, or even weeks.

The model's performance confirms the importance of inference time scaling.

OpenAI has refined data synthesizing skills and training techniques.

The model's Chain of Thought is not as deeply baked as in other models.

There is potential for the model to be over-evaluated.

The full 01 model is not yet available for public use.

Demos of the 01 preview model are available for review.