This article describes the four main approaches to building reasoning models, or how we can enhance LLMs with reasoning capabilities. I hope this provides valuable insights and helps you navigate the rapidly evolving literature and hype surrounding this topic.

In 2024, the LLM field saw increasing specialization. Beyond pre-training and fine-tuning, we witnessed the rise of specialized applications, from RAGs to code assistants. I expect this trend to accelerate in 2025, with an even greater emphasis on domain- and application-specific optimizations (i.e., "specializations").

The development of reasoning models is one of these specializations. This means we refine LLMs to excel at complex tasks that are best solved with intermediate steps, such as puzzles, advanced math, and coding challenges. However, this specialization does not replace other LLM applications. Because transforming an LLM into a reasoning model also introduces certain drawbacks, which I will discuss later.

To give you a brief glimpse of what's covered below, in this article, I will:

1. Explain the meaning of "reasoning model"

2. Discuss the advantages and disadvantages of reasoning models

3. Outline the methodology behind DeepSeek R1

4. Describe the four main approaches to building and improving reasoning models

5. Share thoughts on the LLM landscape following the DeepSeek V3 and R1 releases

6. Provide tips for developing reasoning models on a tight budget

I hope you find this article useful as AI continues its rapid development this year!

How do we define "reasoning model"?

If you work in AI (or machine learning in general), you are probably familiar with vague and hotly debated definitions. The term "reasoning models" is no exception. Eventually, someone will define it formally in a paper, only for it to be redefined in the next, and so on.

In this article, I define "reasoning" as the process of answering questions that require complex, multi-step generation with intermediate steps. For example, factual question-answering like "What is the capital of France?" does not involve reasoning. In contrast, a question like "If a train is moving at 60 mph and travels for 3 hours, how far does it go?" requires some simple reasoning. For instance, it requires recognizing the relationship between distance, speed, and time before arriving at the answer.