The Hierarchical Reasoning Model (HRM) is a novel AI architecture designed to efficiently perform complex sequential reasoning tasks by mimicking how the human brain processes information at multiple hierarchical levels and timescales. It consists of two interconnected recurrent modules:
- A high-level module that operates slowly to handle abstract, strategic planning.
- A low-level module that runs quickly to perform detailed, local computations based on the high-level plan.
This separation allows the model to achieve significant computational depth and handle long, complex reasoning sequences within a single forward pass, without requiring large amounts of training data or explicit supervision of intermediate reasoning steps.
HRM excels at tasks like solving complex Sudoku puzzles, optimal pathfinding in large mazes, and performing well on the Abstraction and Reasoning Corpus (ARC), which is a benchmark for measuring general intelligence capabilities. Remarkably, it attains high performance using only 27 million parameters and about 1,000 training examples, far fewer than typical large language models.
Key features include:
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Hierarchical convergence: The low-level module converges to a local solution, which is integrated by the high-level module to update strategies and refine further processing.
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Adaptive Computational Time (ACT): HRM dynamically adjusts the amount of computation depending on task complexity, improving efficiency.
It does not rely on large-scale pretraining or chain-of-thought supervision. HRM’s internal dynamic reasoning processes can be decoded and visualized, offering interpretability advantages over other neural reasoning methods. Overall, HRM represents a brain-inspired approach toward universal and general-purpose AI reasoning systems, offering substantial computational efficiency and stronger reasoning capabilities compared to larger, conventional models.
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