Author(s): Florian June

Originally published on Towards AI.

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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.

In the 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.

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].

In this article, we introduce 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.

Specifically, 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.

As in my previous article (O1 Replication Journey Part 1: From Shortcut Hunters to True Explorers), we’ll break this article into two main parts: training data and… Read the full blog for free on Medium.

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