Exactly!

> It uses two interdependent recurrent modules: a *high-level module* for abstract, slow planning and a *low-level module* for rapid, detailed computations. This structure enables HRM to achieve significant computational depth while maintaining training stability and efficiency, even with minimal parameters (27 million) and small datasets (~1,000 examples).

> HRM outperforms state-of-the-art CoT models on challenging benchmarks like Sudoku-Extreme, Maze-Hard, and the Abstraction and Reasoning Corpus (ARC-AGI), where CoT methods fail entirely. For instance, it solves 96% of Sudoku puzzles and achieves 40.3% accuracy on ARC-AGI-2, surpassing larger models like Claude 3.7 and DeepSeek R1.

Erm what? How? Needs a computer and sitting down.

Yeah, that was pretty much my reaction. I will need time on a computer too.

The repo is at https://github.com/sapientinc/HRM .

I love it when authors publish working code. It's usually a good sign. If the code does what the authors claim, no one can argue with it!

Is it talking about fine tuning existing models with 1000 examples to beat them in those tasks?