Less is More: Recursive Reasoning with Tiny Networks
Authors: name: Alexia Jolicoeur-Martineau
Organization: Samsung  AI Lab (SAIL) Montreal

Hierarchical Reasoning Model (HRM) is a novel approach using two small neural
networks recursing at different frequencies. This biologically inspired method
beats Large Language models (LLMs) on hard puzzle tasks such as Sudoku, Maze,
and ARC-AGI while trained with small models (27M parameters) on small data
(around 1000 examples). HRM holds great promise for solving hard problems with
small networks, but it is not yet well understood and may be suboptimal. We
propose Tiny Recursive Model (TRM), a much simpler recursive reasoning approach
that achieves significantly higher generalization than HRM, while using a
single tiny network with only 2 layers. With only 7M parameters, TRM obtains
45% test-accuracy on ARC-AGI-1 and 8% on ARC-AGI-2, higher than most LLMs
(e.g., Deepseek R1, o3-mini, Gemini 2.5 Pro) with less than 0.01% of the
parameters.