O1 Replication Journey Part 2: Surpassing O1-Preview through Simple Distillation
Author: Florian June
About the Article
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The Importance of Training Data and Methods
In my view, any kind of learning boils down to two key elements: training data and training methods. For enhancing LLM reasoning or replicating OpenAI o1, obtaining long-thought chains as training data is crucial.
Previous Article: Tree Search as a Method for Generating Training Data
In our previous article (O1 Replication Journey Part 1: From Shortcut Hunters to True Explorers), we explored tree search as a method for generating training data. While tree search is effective, it comes with high computational costs and long processing times.
Introducing Distillation as an Alternative Method
Figure 1: Different methods of collecting the long thought data. The distillation method offers a cost-effective and reliable approach to obtaining high-quality data. [Source]
Main Idea: Obtaining Training Data through Distillation
In this article, we introduce the O1 Replication Journey — Part 2: Surpassing O1-preview through Simple Distillation Big Progress or Bitter Lesson?, where the core idea is to obtain training data through distillation.
Fine-Tuning a Base LLM with Distilled Samples
By fine-tuning a base LLM with tens of thousands of samples distilled from o1’s long-thought chains, it’s possible to outperform o1-preview on the AIME (American Invitational Mathematics Examination) — all with surprisingly low technical complexity.
Conclusion
The article concludes that distillation is a cost-effective and reliable method for obtaining high-quality training data, which can lead to improved performance on complex tasks. The use of distillation can be an attractive alternative to tree search, especially for those with limited computational resources.
FAQs
Q: What is the main idea behind this article?
A: The main idea is to obtain training data through distillation and fine-tuning a base LLM with distilled samples to outperform o1-preview on the AIME.
Q: What is the advantage of using distillation over tree search?
A: Distillation is a cost-effective and reliable method, whereas tree search is effective but comes with high computational costs and long processing times.
Q: Can distillation be used for other complex tasks?
A: Yes, distillation can be used as an alternative method for obtaining training data, which can lead to improved performance on other complex tasks.
