Introduction to the 01 Preview from OpenAI
- The 01 preview represents a significant advancement in AI model training and capabilities.
- The foundational objective of language models is to predict the next word, termed as Paradigm 1.
- Paradigm 2 introduced objectives to make models honest, harmless, and helpful.
- Paradigm 3, represented by the 01 series, aims to reward answers that are objectively correct.
"The foundational original objective of language models is to model language; it's to predict the next word."
- Language models initially focused on predicting the next word in a sequence.
"We wanted models to be honest, harmless, and helpful."
- The second paradigm aimed to make models provide answers that are not only likely but also useful and safe.
"01 for me at least represents Paradigm 3; we want to reward answers that are objectively correct."
- The third paradigm focuses on rewarding models for providing objectively correct answers.
Chain of Thought and Reinforcement Learning
- Models can be prompted to generate a "Chain of Thought" by asking them to think step by step.
- Feeding models thousands of examples of human step-by-step reasoning works but is not optimal.
- OpenAI discovered that training models to generate their own chains of thought using reinforcement learning (RL) is more effective.
"Most of us might be aware that you can get models to output what's called a Chain of Thought by asking models, for example, to think step by step."
- Prompting models to think step by step can generate longer outputs with reasoning steps.
"If you train the model using RL to generate its own chain of thoughts, it can do even better than having humans write chains of thought for it."
- Training models to generate their own reasoning steps using RL produces better results than human-provided examples.
Creative Generation and Grading Outputs
- Models can be encouraged to generate diverse outputs by adjusting their "temperature."
- Researchers can then grade these diverse outputs to identify correct answers.
- The correct outputs are used to fine-tune the model, thus reinforcing learning.
"How about we go up to the model and whisper in its ear, 'Get really creative. Don't worry as much about predicting the next word super accurately.'"
- Encouraging models to generate diverse outputs can lead to creative solutions.
"We take those that work, those that produce the correct answer in mathematics, science, coding, and fine-tune the model on those correct answers with correct reasoning steps."
- Correct outputs are used to further train the model, enhancing its accuracy and reasoning.
Marriage of Train Time Compute and Test Time Compute
- Train time compute involves the fine-tuning or training of a model.
- Test time compute refers to the model's performance when generating outputs.
- Combining these two aspects leads to improved results, especially in technical domains.
"Train time compute, the fine-tuning or training of a model, and what's called test time compute, that thinking time."
- The combination of training and testing computations leads to better model performance.
"More time to think equals better results, but then train or fine-tune the model or generator on correct outputs and reasoning steps, and that also produces a noticeable increase."
- Allowing models more time to think and fine-tuning them on correct outputs both contribute to improved results.
Reasoning and Humanlike Intelligence
- The AI's reasoning is not humanlike but can still be highly effective.
- The analogy of a librarian is used to explain the AI's functioning.
"Think of a librarian. You're going up to this librarian because you have a question you want answered."
- The librarian analogy helps illustrate how the AI processes and retrieves information.
"The original chat GPT was a very friendly librarian, but it would often bring you the wrong book or maybe the right book but point to the wrong paragraph."
- Earlier models were friendly but often inaccurate in providing the correct information.
Conclusion
- The 01 preview from OpenAI marks a significant step forward in AI model training and capabilities.
- By integrating creative generation, reinforcement learning, and a combination of train and test time computations, the models achieve higher accuracy and better reasoning.
- While not humanlike, the AI's reasoning abilities are effective and continuously improving.
The Librarian Analogy for AI Models
- AI as Librarians: The 01 series models are likened to librarians who take meticulous notes on which books (data) successfully answer questions.
- Granular Data Tracking: These models track data down to the chapter, paragraph, and line level.
- Lack of Understanding: The models can present information but do not truly understand it.
- Training Data Limitations: If a question is outside the model's training data, the model will likely provide irrelevant information.
- Reluctance to Admit Ignorance: The model is unlikely to say "I don't know" and will instead provide potentially irrelevant data.
"The 01 series of models are much better librarians. They've been taking notes on what books successfully answered the questions that guests had and which ones didn't, down to the level not just of the book but the chapter, the paragraph, and the line."
- The 01 models track data with high granularity, much like a meticulous librarian.
"The librarian doesn't actually understand what it's presenting."
- The AI models do not possess true understanding, only the ability to present information.
"If you ask a question about something that's not in the model's training data, the librarian will screw up."
- The models fail when asked questions outside their training data.
Philosophical Implications of AI Understanding
- Philosophical Questions: The discussion touches on whether it matters if AI truly understands what it presents.
- Human Brain Analogy: We do not fully understand the human brain, yet it functions effectively.
"We don't even understand how the human brain works."
- The complexity of understanding AI is compared to the complexity of understanding the human brain.
Training Data and Domain-Specific Performance
- Training Data Sufficiency: AI models struggle in domains with ample data but no clear right or wrong answers.
- Performance Variability: The 01 models show performance boosts in domains with clear answers but regress in personal writing and other subjective areas.
"In domains with correct and incorrect answers largely, you can see the performance boost. In areas with harder to distinguish correct or incorrect answers, much less of a boost, in fact, a regress in personal writing."
- The models perform better in objective domains and struggle in subjective ones.
Hidden Chains of Thought in AI Models
- Chains of Thought: The 01 models use hidden chains of thought to arrive at answers, which are not visible to users.
- Competitive Advantage: OpenAI keeps these chains hidden to maintain a competitive edge.
"We can't actually see those chains of thought. If you've used 01 for sure, you do see a summary and of course the output, but not the true chains of thought that led it to that output."
- Users cannot see the internal reasoning processes of the 01 models.
"OpenAI admits that part of the reason for that is their own competitive advantage."
- OpenAI hides the chains of thought to protect their competitive advantage.
Improved Serial Calculations
- Serial Calculations: The 01 models excel in breaking down complex questions into smaller computational steps.
- Scratch Pad Analogy: The models use a metaphorical scratch pad to work out long computations.
"That ability to break down long or confusing questions into a series of small computational steps is why I think that 01 preview gets questions like these correct most of the time."
- The models effectively handle complex questions by breaking them down into smaller steps.
Limitations of Training Data
- Data Dependency: The models still fail when the required data is not in their training set.
- Fitting a Curve: The broader paradigm of fitting a curve to a distribution still applies to these models.
"If those reasoning steps or facts are not in the training data, they're not in distribution, it still will fail."
- The models are limited by the data they have been trained on and fail outside of that scope.
"01 is still not a departure from the broader paradigm of fitting a curve to a distribution."
- The fundamental approach of the models remains fitting a curve to the available data distribution.
Foundation Models for the Physical World
- There is no existing foundation model for the physical world.
- Banks of "correct answers" for real-world tasks are lacking, which affects model performance on simple benchmarks.
"We don't have those banks and banks of quote correct answers for real-world tasks, and that's partly why models flop on simple benchmarks."
- The absence of comprehensive training data for real-world tasks is a significant limitation.
01 Preview Model vs. GPT-4
- 01 Preview is capable of solving problems that GPT-4 could not.
- An example given involves stacking blocks, a task that previous models struggled with due to complexity.
"You should start to notice a pattern in those questions that 01 Preview is now getting right where GPT-4 couldn't."
- 01 Preview can handle tasks requiring complex, serial calculations and computations better than previous models.
- Training data significantly influences model performance.
- 01 Preview can understand scenarios involving nuanced relationships, such as the surgeon riddle.
"The surgeon who is the boy's father says, 'I can't operate on this boy; he's my son.' Who is the surgeon to the boy?"
- The correct answer is that the surgeon is the boy's other father, illustrating 01 Preview's improved reasoning capabilities.
Exam-Style Knowledge vs. Real-World Capabilities
- Exam-style benchmarks do not equate to real-world problem-solving abilities.
- Real-world tasks require more than just factual knowledge; they require reasoning and understanding of context.
"Exam-style knowledge benchmarks in particular, rather than true reasoning benchmarks, do not equal real-world capabilities."
- Emphasizes the need for models to go beyond rote learning and engage in true reasoning.
Reinforcement Learning in 01 Training
- 01 Preview's training included an extra layer of reinforcement learning, setting it apart from GPT-4.
- No amount of prompt engineering on GPT-4 can match the performance of 01 Preview.
"The training of 01 was fundamentally different from GPT-4; that extra layer of reinforcement learning means that no amount of prompt engineering on GPT-4 can match its performance."
- Reinforcement learning allows the model to optimize its reasoning steps to achieve the desired result.
Reasoning Steps and Optimization
- 01 Preview can piece together reasoning steps in ways that may not be immediately legible to humans.
- The model is optimized to find the right answer, even if the reasoning process is opaque.
"When you know the right chain of thought, you can compute anything."
- The focus is on achieving the correct outcome, regardless of the transparency of the reasoning process.
Comparison to Chess Model Development
- The evolution of chess models, like Stockfish, parallels the development of AI reasoning models.
- Transition from handcrafted evaluation functions to fully neural network-based approaches.
"By crafting its own reasoning steps and being optimized to put them together in the most effective fashion, we may end up with reasoning that we ourselves couldn't have come up with."
- Highlights the potential for AI to develop novel reasoning methods that humans might not conceive.
Multimodal Reasoning and Simulation
- Emitting chains of thought can extend to different modalities, such as video generation.
- Models could predict sequences of events or pixels, enhancing their reasoning capabilities.
"This approach of emitting chains of thought can be extended into different modalities, where the chain of thought is basically a simulation of the world."
- Multimodal reasoning could lead to significant improvements in understanding and generating complex sequences.
Reinforcement Learning and Fine-Tuning
- Fine-tuning models on their own successful outputs is a key strategy for improvement.
- Reinforcement learning can be used to refine models based on their performance in generating correct answers.
"It involves fine-tuning a model on the outputs it generated that happen to work, keep going until you generate rationals that get the correct answer, and then fine-tune on all of those rationals."
- This iterative process of refining based on successful outputs enhances model accuracy and reasoning.
Predictions and Future Directions
- Previous predictions about the development of AI models have been accurate.
- Future research will likely continue to focus on reinforcement learning and fine-tuning to improve model performance.
"If that prediction isn't worthy of a like on YouTube or preferably joining AI insiders, then I don't know what is."
- The trajectory of AI development is moving towards more sophisticated reasoning and problem-solving capabilities.
Reinforcement Learning and Creativity
- Reinforcement learning (RL) is highlighted as a key driver of creativity in AI.
- RL systems can develop novel solutions that humans might not understand.
- The potential risks of RL, especially over long or medium time horizons, are emphasized.
"Reinforcement learning is creative. Reinforcement learning has a much more significant challenge; it is creative. Reinforcement learning is actually creative."
- Reinforcement learning is inherently creative, which poses both opportunities and challenges.
"All the stunning examples of creativity in AI come from a reinforcement learning system."
- RL systems are behind many of the most innovative and creative AI applications.
"Alpha Zero has invented a whole new way of playing a game that humans have perfected for thousands of years."
- Example of Alpha Zero, which used RL to develop new strategies in games like chess and Go.
"It is reinforcement learning that can come up with creative solutions to problems, solutions which we might not be able to understand at all."
- RL can produce solutions that are difficult for humans to comprehend.
"This does not mean that this problem is unsolvable, but it means that it is a problem."
- While RL's creativity is a challenge, it is not an insurmountable one.
Weaknesses in Spatial Reasoning
- Acknowledgement of current AI limitations in spatial reasoning.
- The complexity of the world remains a barrier to achieving Artificial General Intelligence (AGI).
"The development is likely a big step forward for narrow domains like mathematics but is in no way yet a solution for AGI."
- Advances are significant for specific fields but do not yet solve broader AGI challenges.
"The world is still a bit too complex for this to work yet."
- The complexity of real-world environments continues to be a significant hurdle.
Let's Verify Step by Step Approach
- Introduction of the "Let's Verify Step by Step" method to improve AI model accuracy.
- Focus on verifying individual reasoning steps rather than just final answers.
"They came out with let's verify step by step in this paper by getting a verifier or reward model to focus on the process, the P, instead of the outcome, the O, results were far more dramatic."
- Emphasizes the shift from outcome-based to process-based verification for better results.
"The problem that they noticed with their approach back in 2021 was that their models were rewarding correct solutions, but sometimes there would be false positives getting to the correct final answer using flawed reasoning."
- Identifies the issue of models reaching correct answers through incorrect reasoning.
Enhanced Inference Time Compute
- Discussion on the potential of using more computational power during inference to improve model accuracy.
- Speculation on future improvements with increased compute resources.
"Notice how the graph is continuing to rise if they just had more let's say test time compute this could continue rising higher."
- Suggests that additional computational resources could further enhance model performance.
"I actually speculated on that back on June the 1st that difference of about 10% is more than half of the difference between GPT-3 and GPT-4."
- Highlights the significant impact that increased compute could have on model performance.
Reward Model Training
- Explanation of training a reward model to identify correct reasoning steps.
- The reward model's ability to generalize beyond mathematics to other subjects.
"They trained a reward model to notice the individual steps in a reasoning sequence. That reward model then got very good at spotting erroneous steps."
- Describes the training process for the reward model to improve step-by-step verification.
"The method somewhat generalized out of distribution, going beyond mathematics to boost performance in chemistry, physics, and other subjects."
- Notes the reward model's ability to enhance performance in various fields.
Use of Verifiers in Training
- Speculation that verifiers were used in training the 01 model family.
- Importance of correct reasoning steps in the training process.
"My theory is the only answers for which every reasoning step was correct and the final answer were used to train or fine-tune the 01 family."
- Suggests a rigorous training process focusing on correct reasoning steps for the 01 models.
High Temperature Solutions
- Discussion on the use of high temperature for generating creative solutions.
- Connection to the verification process and model training.
"Higher temperature was optimal for generating those creative chains of thought. That was suggested as early as 2021 at OpenAI."
- Indicates the early recognition of the benefits of high-temperature solutions for creativity.
"Verification consists of sampling multiple high temperature solutions and then it goes on about verification."
- Describes the verification process involving high-temperature solution sampling.
Government Interest and Support
- Recognition of government interest in AI development for national security and economic interests.
- Mention of AI projects being shown to the White House and their importance.
"The White House is certainly taking all of this quite seriously. They were shown strawberry and 01 earlier this year."
- Highlights the government's serious consideration of AI advancements.
"They now describe how AI data center development and promoting it and funding it reflects the importance of these projects to American national security and economic interests."
- Emphasizes the strategic importance of AI projects to the US government.