What Are the Differences Between Pre-training and Post-training in AI?

TL;DR

Pre-training creates models that can generate diverse web-like content by predicting the next token, while post-training refines them to perform specific tasks, such as chat assistance. In the next five years, models are expected to significantly enhance their capabilities, allowing them to handle complex tasks autonomously, such as coding projects or scientific research.

Transcript

Today I have the pleasure to speak with John  Schulman, who is one of the co-founders of OpenAI   and leads the post-training team here. He also  led the creation of ChatGPT and is the author   of many of the most important and widely  cited papers in AI and RL, including PPO   and many others. John, really excited to chat  with you. Thanks for com... Read More

Key Insights

• 😑 Pre-training trains models to imitate web content, while post-training targets specific behaviors for tasks like chat assistance.
• 😑 Models trained in pre-training have the ability to generate various content personas.
• 👨‍🔬 In the next five years, models are expected to become more capable of complex tasks, such as coding projects and scientific research.
• 🧑‍🏭 The model's ability to act coherently for longer periods of time is crucial for more complex tasks.
• 😑 The generalization from pre-training experiences can help models recover from errors and deal with edge cases.
• 🚂 Long-horizon tasks are expected to require more model intelligence and be more expensive to train.
• ⛔ Coordinating among entities and establishing reasonable limits on deployment and training would be important in the event of AGI development.
• 🦺 Designing AI systems with alignment and careful oversight is essential to avoid potential risks and ensure safety in deployment.
• ✳️ Effective monitoring and evaluation of AI systems, along with careful decision-making, are necessary to address potential risks and ensure alignment with human values.
• 💄 The future deployment of AI systems may involve coordination, regulation, and the involvement of humans in important decision-making processes.
• 😑 The pathway to advanced AI capabilities may involve combining pre-training and post-training approaches while considering the needs of different stakeholders.
• 👨‍🔬 The replication of social science experiments using AI models and the exploration of correlations between traits could be an exciting area of research.

Questions & Answers

Q: What does pre-training and post-training entail in AI models?

Pre-training involves training models to imitate web content, while post-training focuses on narrowing the range of behaviors for more specific tasks like chat assistance.

Q: What is the goal of pre-training?

The goal of pre-training is to train models to generate content that resembles random web pages and to assign probabilities to each output.

Q: How does post-training differ from pre-training?

Post-training targets a narrower range of behaviors, aiming to make models behave like chat assistants by answering questions and performing tasks.

Q: What improvements can be expected in the next five years?

Models are expected to improve in the next five years, becoming more capable of involved tasks and able to perform tasks that would normally take humans hours or days to complete.

Summary & Key Takeaways

• Pre-training trains models to generate content that resembles web pages, while post-training narrows the focus to more specific tasks like chat assistance.

• Models trained in pre-training have the capability to generate various content personas, while post-training targets a narrower range of behaviors for chat assistance.

• In the next five years, models are expected to improve and become more capable of complex tasks like coding projects and scientific research.