Chinese Researchers Reveal How OpenAI o3 Works!

1. Chinese researchers have uncovered the secrets of OpenAI's models.

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The research from Fudan University reveals how OpenAI's 01 and 03 models achieve advanced reasoning capabilities, classified as AGI.

• The study focuses on the concept of 'test time compute', which enhances model performance during inference.
• It identifies four critical elements that contribute to the models' thinking abilities.
• The findings aim to open-source the understanding of these advanced AI models.

2. OpenAI's models are progressing towards AGI.

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The 01 model represents a significant milestone, achieving reasoning capabilities comparable to PhD-level proficiency.

• It is part of OpenAI's roadmap towards artificial general intelligence, moving through defined stages.
• The model can perform human-like reasoning, including clarifying questions and exploring solutions.
• Current advancements suggest we may be nearing the third stage of AI development.

3. Test time compute is essential for model performance.

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The ability of models to think during inference time significantly boosts their performance on complex tasks like mathematics and scientific reasoning.

• More computation time during inference leads to better results.
• This approach marks a shift from traditional self-supervised learning to reinforcement learning.
• The 01 model exemplifies this new paradigm in AI development.

4. Four key aspects define the thinking models' functionality.

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The researchers identified policy initialization, reward design, search, and learning as critical components of the models.

• Policy initialization involves pre-training and fine-tuning to prepare the model for tasks.
• Reward design is crucial for guiding the model's learning process.
• Search capabilities during inference allow the model to explore multiple solutions.

5. Human-like reasoning behaviors enhance model capabilities.

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The models incorporate behaviors such as problem analysis, task decomposition, and self-correction to improve reasoning.

• These behaviors allow the model to break down complex problems into manageable tasks.
• Self-evaluation and correction enable the model to refine its responses iteratively.
• The ability to propose alternative solutions is crucial for overcoming reasoning obstacles.

6. Exposure to programming code enhances AI reasoning capabilities.

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Research shows that exposure to programming code significantly improves a model's logical reasoning skills, making it more effective in problem-solving.

• Structured logical data helps strengthen reasoning capabilities.
• Self-reflection allows models to assess and improve their outputs.
• Combining code exposure with self-reflection leads to better performance.

7. Reward design is crucial for AI learning.

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AI models utilize different reward systems, such as outcome rewards and process rewards, to learn from their outputs effectively.

• Outcome rewards assess the final output, while process rewards evaluate each step taken.
• Process rewards provide feedback on intermediate steps, allowing for targeted improvements.
• This dual approach enhances learning efficiency in complex problem-solving.

8. Realistic environments provide valuable feedback for AI.

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Interacting with realistic environments allows AI models to receive accurate feedback on their outputs, improving their learning process.

• Running generated scripts in compilers can validate AI outputs.
• In cases where real-time feedback isn't available, reward models simulate expected outcomes.
• This feedback loop is essential for refining AI performance.

9. Search strategies enhance AI problem-solving.

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AI models employ various search strategies to explore potential solutions and improve output quality during training and inference.

• Tree search techniques allow for broader exploration of solutions.
• Sequential revisions refine answers based on previous outputs.
• Effective search strategies can enable smaller models to outperform larger ones.

10. Reinforcement learning can achieve superhuman performance.

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Reinforcement learning allows AI to learn from trial and error, potentially surpassing human capabilities in specific tasks.

• AI models can discover new strategies through extensive self-play.
• The example of AlphaGo illustrates how AI can innovate beyond human understanding.
• Removing human feedback can lead to more efficient learning processes.

11. Future directions for OpenAI's o3 include adapting to general domains.

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Researchers are exploring how to adapt o3 to general domains like math and science, especially when answers are not clear.

• Adapting to clear domains is easier, but unknown problems pose challenges.
• The goal is to enable models to think through problems without known answers.
• This adaptation is crucial for expanding the model's applicability.

12. Introducing multiple modalities is a key focus for OpenAI.

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OpenAI is working on integrating multiple modalities into o3, enhancing its capabilities.

• Multiple modalities will allow the model to process different types of data.
• This integration is expected to improve the model's performance in various tasks.
• OpenAI has already discussed this direction in their research.