Hierarchical Reasoning Model

Hierarchical Reasoning Model

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The Hierarchical Reasoning Model (HRM) is a brain-inspired AI architecture that achieves exceptional reasoning capabilities with only 27 million parameters, using two interdependent recurrent modules for abstract planning and detailed computations.
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Hierarchical Reasoning Model

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Updated:Aug 16, 2025

What is Hierarchical Reasoning Model

The Hierarchical Reasoning Model (HRM) is a novel recurrent architecture developed by Sapient Intelligence that revolutionizes AI reasoning capabilities. Released in July 2025, HRM draws inspiration from the hierarchical and multi-timescale processing patterns observed in the human brain. Unlike traditional large language models that rely on Chain-of-Thought (CoT) techniques, HRM operates efficiently with minimal training data and without pre-training requirements. The model demonstrates remarkable performance on complex reasoning tasks, including solving extreme Sudoku puzzles and optimal pathfinding in large mazes, while using only 1,000 training samples.

Key Features of Hierarchical Reasoning Model

The Hierarchical Reasoning Model (HRM) is a brain-inspired AI architecture that uses two interdependent recurrent modules - a high-level module for abstract planning and a low-level module for detailed computations - to achieve complex reasoning capabilities. With only 27 million parameters and trained on just 1,000 examples without pre-training, HRM can solve challenging tasks through hierarchical processing, temporal separation, and recurrent connectivity, outperforming much larger language models while being more efficient and stable.
Hierarchical Dual-Module Architecture: Features two coupled recurrent modules operating at different timescales - a high-level module for slow, abstract planning and a low-level module for fast, detailed computations
Minimal Training Requirements: Achieves exceptional performance using only 1,000 training samples without requiring pre-training or Chain-of-Thought data
Efficient Parameter Usage: Accomplishes complex reasoning tasks with just 27 million parameters, significantly fewer than traditional large language models
Single Forward Pass Processing: Executes sequential reasoning tasks in one forward pass without needing explicit supervision of intermediate steps

Use Cases of Hierarchical Reasoning Model

Complex Puzzle Solving: Solves extreme Sudoku puzzles and other complex mathematical/logical puzzles with near-perfect accuracy
Pathfinding Optimization: Finds optimal paths in large mazes and complex navigation scenarios efficiently
Abstract Reasoning Tasks: Performs well on the Abstraction and Reasoning Corpus (ARC), demonstrating capabilities in general intelligence tasks

Pros

Highly efficient with minimal parameter count and training data requirements
Stable training process without convergence issues
Superior performance on complex reasoning tasks compared to larger models

Cons

May experience late-stage overfitting in small-sample scenarios
Shows accuracy variance of ±2 points in small-sample learning
Requires specific GPU configurations and CUDA extensions for optimal performance

How to Use Hierarchical Reasoning Model

Install Prerequisites: Install CUDA 12.6, PyTorch with CUDA support, and additional packages for building extensions. Run: wget CUDA installer, install CUDA, set CUDA_HOME, install PyTorch, and install packaging dependencies
Install FlashAttention: For Hopper GPUs: Clone flash-attention repo and install FlashAttention 3. For Ampere or earlier GPUs: Install FlashAttention 2 via pip install flash-attn
Install Python Dependencies: Run 'pip install -r requirements.txt' to install all required Python packages
Set up Weights & Biases: Set up W&B for experiment tracking by running 'wandb login' and ensuring you're logged in to your account
Prepare Dataset: Build the dataset for your specific task. For example, for Sudoku: Run 'python dataset/build_sudoku_dataset.py' with appropriate parameters for dataset size and augmentation
Start Training: Launch training with appropriate parameters. Example for Sudoku: 'OMP_NUM_THREADS=8 python pretrain.py data_path=data/sudoku-extreme-1k-aug-1000 epochs=20000 eval_interval=2000 global_batch_size=384 lr=7e-5'
Monitor Training: Track training progress through W&B interface, monitoring eval/exact_accuracy metric
Evaluate Model: Run evaluation using 'torchrun --nproc-per-node 8 evaluate.py checkpoint=<CHECKPOINT_PATH>' and analyze results through provided notebooks
Use Pre-trained Checkpoints: Alternatively, download pre-trained checkpoints from HuggingFace for ARC-AGI-2, Sudoku 9x9 Extreme, or Maze 30x30 Hard tasks

Hierarchical Reasoning Model FAQs

HRM is a novel recurrent architecture inspired by the hierarchical and multi-timescale processing in the human brain. It features two interdependent recurrent modules: a high-level module for slow, abstract planning, and a low-level module for rapid, detailed computations. It can execute sequential reasoning tasks in a single forward pass without explicit supervision.

Hierarchical Reasoning Model Video

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