LatestMachine LearningO1 Replication Journey Part 2: Let a Great Teacher Guide Students 0 like March 5, 2025Share this postLast Updated on March 5, 2025 by Editorial TeamAuthor(s): Florian June Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.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 o1s long-thought chains, its 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), well break this article into two main parts: training data and Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post