OpenAI o1 LLM

A OpenAI o1 LLM is an OpenAI GPT-4 multimodal LLM released on 2024-09-12.

• Context:
  • It can (typically) spend more time thinking before providing responses.
  • ...
  • It can process text, images, and audio data to handle complex reasoning tasks.
  • It can provide enhanced real-time speech recognition and advanced text-to-speech capabilities.
  • ...
• Example(s):
  • ...
• Counter-Example(s):
  • GPT-4o, and GPT-4o which does not possess the same depth in complex reasoning tasks.
  • Claude 4, which is designed primarily for conversational AI rather than rigorous reasoning tasks.
  • Gemini 2, which focuses more on general-purpose NLP rather than specialized reasoning tasks.
• See: OpenAI LLM Model, Foundation Neural Model, GPT-4 Turbo

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References

2024

• (OpenAI, 2024) ⇒ OpenAI. (2024). "Introducing OpenAI o1 LLM."
  • NOTES:
    • It is a multimodal large language model designed for advanced reasoning in challenging domains such as physics and computational tasks.

2024

• https://openai.com/index/learning-to-reason-with-llms/
  • NOTES:
    • o1 is a new large language model trained with reinforcement learning to perform complex reasoning, producing a detailed internal chain of thought before responding to users.
    • The model significantly outperforms GPT-4o on challenging reasoning benchmarks across math, coding, and science exams, demonstrating advanced problem-solving capabilities.
    • On the 2024 AIME exams:
      • o1 averaged 74% accuracy with a single attempt per problem.
      • This performance placed it among the top 500 students nationally.
      • It exceeded the cutoff for the USA Mathematical Olympiad.
    • In competitive programming, o1 ranks:
      • In the 89th percentile on Codeforces.
      • Achieving an Elo rating of 1807, outperforming 93% of human competitors.
    • o1 exceeds human PhD-level accuracy on a benchmark of physics, biology, and chemistry problems (GPQA), surpassing the performance of human experts in these domains.
    • Through reinforcement learning, o1:
      • Refines its chain of thought.
      • Learns to recognize and correct mistakes.
      • Breaks down complex steps.
      • Adopts different approaches when necessary.
    • The model's performance improves with:
      • Increased reinforcement learning (train-time compute).
      • More time allocated for reasoning (test-time compute), showing scalability with computational resources.
    • An early version, o1-preview:
      • Is available in ChatGPT and to trusted API users.
      • Ongoing work aims to make the model as user-friendly as current offerings.
    • Human preference evaluations show:
      • o1-preview is strongly preferred over GPT-4o in reasoning-intensive tasks like data analysis, coding, and math.
      • It is less preferred in some natural language tasks.
    • Chain of thought reasoning enhances safety and alignment by:
      • Enabling the model to reason about safety rules internally.
      • Making it more robust to unexpected scenarios.
    • To balance user experience and safety:
      • OpenAI provides a model-generated summary of the chain of thought.
      • Instead of revealing the raw reasoning process to users.

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