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Computer Science > Artificial Intelligence

arXiv:2506.21734 (cs)
[Submitted on 26 Jun 2025 (v1), last revised 22 Jul 2025 (this
version, v2)]

Title:Hierarchical Reasoning Model

Authors:Guan Wang, Jin Li, Yuhao Sun, Xing Chen, Changling Liu, Yue
Wu, Meng Lu, Sen Song, Yasin Abbasi Yadkori
View a PDF of the paper titled Hierarchical Reasoning Model, by Guan
Wang and 8 other authors
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    Abstract:Reasoning, the process of devising and executing complex
    goal-oriented action sequences, remains a critical challenge in
    AI. Current large language models (LLMs) primarily employ
    Chain-of-Thought (CoT) techniques, which suffer from brittle task
    decomposition, extensive data requirements, and high latency.
    Inspired by the hierarchical and multi-timescale processing in
    the human brain, we propose the Hierarchical Reasoning Model
    (HRM), a novel recurrent architecture that attains significant
    computational depth while maintaining both training stability and
    efficiency. HRM executes sequential reasoning tasks in a single
    forward pass without explicit supervision of the intermediate
    process, through two interdependent recurrent modules: a
    high-level module responsible for slow, abstract planning, and a
    low-level module handling rapid, detailed computations. With only
    27 million parameters, HRM achieves exceptional performance on
    complex reasoning tasks using only 1000 training samples. The
    model operates without pre-training or CoT data, yet achieves
    nearly perfect performance on challenging tasks including complex
    Sudoku puzzles and optimal path finding in large mazes.
    Furthermore, HRM outperforms much larger models with
    significantly longer context windows on the Abstraction and
    Reasoning Corpus (ARC), a key benchmark for measuring artificial
    general intelligence capabilities. These results underscore HRM's
    potential as a transformative advancement toward universal
    computation and general-purpose reasoning systems.

Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2506.21734 [cs.AI]
          (or arXiv:2506.21734v2 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2506.21734
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Yuhao Sun [view email]
[v1] Thu, 26 Jun 2025 19:39:54 UTC (1,542 KB)
[v2] Tue, 22 Jul 2025 06:45:57 UTC (1,525 KB)
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