Hierarchical Reasoning Model
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Hierarchical Reasoning Model (HRM) is a novel, brain-inspired architecture that replaces CoT prompting with a recurrent model designed for deep, latent computation. It departs from token-level reasoning by using two coupled modules: a slow, high-level planner and a fast, low-level executor, achieving greater reasoning depth and efficiency with only 27M parameters and no pretraining. Despite its small size and minimal training data (~1k examples), HRM solves complex tasks like ARC, Sudoku-Extreme, and 30×30 maze navigation, where CoT-based LLMs fail.
HRM introduces hierarchical convergence, where the low-level module rapidly converges within each cycle, and the high-level module updates only after this local equilibrium is reached. This allows for nested computation and avoids premature convergence typical of standard RNNs.
A 1-step gradient approximation sidesteps memory-intensive backpropagation-through-time (BPTT), enabling efficient training using only local gradient updates, grounded in deep equilibrium models.
HRM implements adaptive computation time using a Q-learning-based halting mechanism, dynamically allocating compute based on task complexity. This allows the model to “think fast or slow” and scale at inference time without retraining.
Experiments on ARC-AGI, Sudoku-Extreme, and Maze-Hard show that HRM significantly outperforms larger models using CoT or direct prediction, even solving problems that other models fail entirely (e.g., 74.5% on Maze-Hard vs. 0% for others).
Analysis reveals that HRM learns a dimensionality hierarchy similar to the cortex: the high-level module operates in a higher-dimensional space than the low-level one (PR: 89.95 vs. 30.22), an emergent trait not present in untrained models.
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