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arXiv:2506.21734cs.AI

Hierarchical Reasoning Model

Guan Wang, Jin Li, Yuhao Sun, Xing Chen, Changling Liu, Yue Wu, Meng Lu, Sen Song, Yasin Abbasi Yadkori

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.

arXiv comments
Journal reference
DOI
Subjects
cs.AIArtificial Intelligence cs.LGMachine Learning

On Hacker News

Linked by 6 identifiable commenters in 6 comments across 6 threads, from Jul 2025 to Oct 2025. Found in 4 HN submissions from Jul 2025 to Jul 2025. Open submission discussions.

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Discussed in comments6 threads

  1. Less is more: Recursive reasoning with tiny networks 1 comment

    • Wow, so not only are the findings from https://arxiv.org/abs/2506.21734 (posted on HN a while back) confirmed, they're generalizable? Intriguing. I wonder if this will pan out in practical use cases, it'd be transformative. Also would possibly instantly void the value of…

      BalinaresView on HNParent context

  2. From multi-head to latent attention: The evolution of attention mechanisms 1 comment

    • There’s the hierarchical reasoning model https://arxiv.org/abs/2506.21734 but it’s very new and largely untested Though honestly I don’t think new neural network architectures are going to get us over this local maximum, I think the next steps forward involve something that’s 1.…

      mxkopyView on HNParent context

  3. Is chain-of-thought AI reasoning a mirage? 1 comment

    • This paper I read from here has an interesting mathematical model for reasoning based on cognitive science. https://arxiv.org/abs/2506.21734 (there is also code here https://github.com/sapientinc/HRM) I think we will see dramatic performance increases on "reasoning" problems…

      mucho_mojoView on HNParent context

  4. Hierarchical Reasoning Model – 1k training samples SoTA reasoning v/s CoT 1 comment

    • Link to paper here https://arxiv.org/pdf/2506.21734 Still reading, but the benchmarks for ARC-AGI-1, ARC-AGI-2, Sudoku-Extreme (9x9), and Maze-Hard (30x30) look impressive.

      munroView on HNParent context

  5. New AI architecture delivers 100x faster reasoning with just 1,000 examples 1 comment

    • sapientinc/HRM: Hierarchical Reasoning Model: https://github.com/sapientinc/HRM : > [...] 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…

      westurnerView on HNParent context

  6. A 27M-param model that solves hard Sudoku/mazes where LLMs fail, without CoT 1 comment

    • Hi HN, We've seen LLMs struggle with complex, multi-step reasoning tasks. The common approach, Chain-of-Thought (CoT), often requires massive datasets, is brittle, and suffers from high latency. To tackle this, we developed the Hierarchical Reasoning Model (HRM), a novel…

      mingli_yuanView on HNParent context

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