Issue #65 LLMs Edwin Dong, Lalit Julapalli
Hierarchical Reasoning Models (HRMs), Simply Explained
Free editionPRO edition
This week’s PRO issue is accompanied by a Colab notebook that includes a simple implementation of an HRM to solve Sudoku puzzles. As always, the code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
In June 2025, a research paper published by Guan Wang and Sapient Intelligence’s team rocked the AI world, showcasing a highly powerful LLM model that blew concurrent reasoning models out of the water.
This new LLM architecture, called the Hierarchical Reasoning Model (HRM), promises extraordinary accuracy along with efficiency and stability, challenging our existing ideas of training models and receiving outputs.
The release went viral within the AI community, with X/Twitter discussions hitting over 4 million views and tens of thousands of likes, while YouTube videos dissecting the work surpassed 475K views.
But unlike typical AI breakthroughs that require massive computational resources and internet-scale datasets, HRM achieves its remarkable results with just 27 million parameters and 1,000 training examples.
This sounds like a lot to the untrained eye, but consider that models like ChatGPT and Llama rely on billions of parameters and huge training data sets.
With the paper only coming out two months ago, there isn’t much supporting evidence, apart from what’s included in the paper, to explore.
For that reason, let’s take a broader view of how HRM works to gain a broader understanding of why the community is viewing it as a potential paradigm shift.
This edition is for PRO members.
