Source URL: https://simonwillison.net/2025/Jan/16/gwern/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting gwern
Feedly Summary: […] much of the point of a model like o1 is not to deploy it, but to generate training data for the next model. Every problem that an o1 solves is now a training data point for an o3 (eg. any o1 session which finally stumbles into the right answer can be refined to drop the dead ends and produce a clean transcript to train a more refined intuition).
— gwern
Tags: o1, generative-ai, inference-scaling, ai, llms
AI Summary and Description: Yes
Summary: The text discusses the use of a model referred to as o1 for generating training data to enhance subsequent models, particularly focusing on its role within the context of generative AI. This insight is critical for professionals in AI and machine learning, particularly those engaged in model training and refinement processes.
Detailed Description:
– The passage highlights a core function of the model o1, which is not primarily to be deployed for direct use but to create high-quality training data for subsequent models, specifically model o3. This indicates a strategic approach to model development, where initial models serve as tools for gathering insights and refining understanding.
– Key points include:
– **Training Data Generation**: o1 generates valuable training data instead of serving as an end-user solution.
– **Feedback Loop**: When o1 tackles various problems, the outcomes, including failures or dead ends, are transformed into data points. These data points become crucial for improving the next iteration (o3).
– **Refined Intuition Creation**: The process of generating clean transcripts based on successful sessions aids in building a more sophisticated understanding or ‘intuition’ for the upcoming models.
– This method underscores the iterative nature of AI development, where insights from one model can significantly enhance future models. For security professionals in AI and cloud computing, focusing on effective data generation and privacy during training processes is essential to comply with regulations and enhance system security.
– Practical implications:
– Understanding how training data is created helps in establishing security protocols around data management.
– Recognizing the potential ethical and compliance challenges in using generated training data, especially if the source data involves sensitive information.
– Engineering teams can apply this knowledge to improve model robustness while ensuring alignment with governance and risk management practices.