Pretraining gives language models their knowledge, and post-training gives them purpose!
But with so many techniques—fine-tuning, instruction tuning, RLHF, test-time reasoning—it’s easy to get lost in the maze.
I recently came across a fantastic paper—LLM Post-Training: A Deep Dive into Reasoning LLMs—that mapped out this space beautifully. So I decided to break it all down into something easier to digest.
📘 In this post, I’ve:
- Categorized key post-training methods (Fine-Tuning, RL, Test-Time Scaling)
- Summarized strengths, challenges, and real-world use cases
- Shared clean, visual tables to help you pick the right technique for your application
This reference might save you a lot of time if you’re building AI products or working with LLMs in production.