Methods and Strategies for Building and Refining Reasoning Models
17 hours ago
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.
My Newsletter: Please Subscribe to my Newsletter Applied Intelligence (1.8k subscriber) 7 days free trial . You can also subscribe for free.
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:
- Explain the meaning of “reasoning model”
