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README.md Copy Chapter

Hierarchical Reasoning Model


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.

Read Our Paper: https://arxiv.org/abs/2506.21734

Join Our Discord Community: https://discord.gg/sapient

Quick Start Guide 🚀 Copy Chapter

Prerequisites ⚙️ Copy Section

Ensure PyTorch and CUDA are installed. The repo needs CUDA extensions to be built. If not present, run the following commands:

Install CUDA 12.6

CUDA_URL=https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_560.35.05_linux.run
wget -q --show-progress --progress=bar:force:noscroll -O cuda_installer.run $CUDA_URL
sudo sh cuda_installer.run --silent --toolkit --override
export CUDA_HOME=/usr/local/cuda-12.6
Install PyTorch with CUDA 12.6

PYTORCH_INDEX_URL=https://download.pytorch.org/whl/cu126
pip3 install torch torchvision torchaudio --index-url $PYTORCH_INDEX_URL
Additional packages for building extensions

pip3 install packaging ninja wheel setuptools setuptools-scm

Then install FlashAttention. For Hopper GPUs, install FlashAttention 3

git clone [email protected]:Dao-AILab/flash-attention.git
cd flash-attention/hopper
python setup.py install

For Ampere or earlier GPUs, install FlashAttention 2

pip3 install flash-attn

Install Python Dependencies 🐍 Copy Chapter

pip install -r requirements.txt

W&B Integration 📈 Copy Chapter

This project uses Weights & Biases for experiment tracking and metric visualization. Ensure you're logged in:

wandb login

Run Experiments Copy Chapter

Quick Demo: Sudoku Solver 💻🗲 Copy Section

Train a master-level Sudoku AI capable of solving extremely difficult puzzles on a modern laptop GPU. 🧩

Download and build Sudoku dataset

python dataset/build_sudoku_dataset.py --output-dir data/sudoku-extreme-1k-aug-1000  --subsample-size 1000 --num-aug 1000
Start training (single GPU, smaller batch size)

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 puzzle_emb_lr=7e-5 weight_decay=1.0 puzzle_emb_weight_decay=1.0

Runtime: ~10 hours on a RTX 4070 laptop GPU

Trained Checkpoints 🚧 Copy Chapter

- ARC-AGI-2
- Sudoku 9x9 Extreme (1000 examples)
- Maze 30x30 Hard (1000 examples)

To use the checkpoints, see Evaluation section below.

Full-scale Experiments 🔵 Copy Chapter

Experiments below assume an 8-GPU setup.

Dataset Preparation Copy Section

Initialize submodules

git submodule update --init --recursive
ARC-1

python dataset/build_arc_dataset.py  # ARC offical + ConceptARC, 960 examples
ARC-2

python dataset/build_arc_dataset.py --dataset-dirs dataset/raw-data/ARC-AGI-2/data --output-dir data/arc-2-aug-1000  # ARC-2 official, 1120 examples
Sudoku-Extreme

python dataset/build_sudoku_dataset.py  # Full version
python dataset/build_sudoku_dataset.py --output-dir data/sudoku-extreme-1k-aug-1000  --subsample-size 1000 --num-aug 1000  # 1000 examples
Maze

python dataset/build_maze_dataset.py  # 1000 examples

Dataset Visualization Copy Section

Explore the puzzles visually:

* Open puzzle_visualizer.html in your browser.
* Upload the generated dataset folder located in data/....

Launch experiments Copy Chapter

Small-sample (1K) Copy Section

ARC-1:

OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py

Runtime: ~24 hours

ARC-2:

OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/arc-2-aug-1000

Runtime: ~24 hours (checkpoint after 8 hours is often sufficient)

Sudoku Extreme (1k):

OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/sudoku-extreme-1k-aug-1000 epochs=20000 eval_interval=2000 lr=1e-4 puzzle_emb_lr=1e-4 weight_decay=1.0 puzzle_emb_weight_decay=1.0

Runtime: ~10 minutes

Maze 30x30 Hard (1k):

OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/maze-30x30-hard-1k epochs=20000 eval_interval=2000 lr=1e-4 puzzle_emb_lr=1e-4 weight_decay=1.0 puzzle_emb_weight_decay=1.0

Runtime: ~1 hour

Full Sudoku-Hard Copy Section

OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/sudoku-hard-full epochs=100 eval_interval=10 lr_min_ratio=0.1 global_batch_size=2304 lr=3e-4 puzzle_emb_lr=3e-4 weight_decay=0.1 puzzle_emb_weight_decay=0.1 arch.loss.loss_type=softmax_cross_entropy arch.L_cycles=8 arch.halt_max_steps=8 arch.pos_encodings=learned

Runtime: ~2 hours

Evaluation Copy Chapter

Evaluate your trained models:

* Check eval/exact_accuracy in W&B.
* For ARC-AGI, follow these additional steps:

OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 evaluate.py checkpoint=<CHECKPOINT_PATH>

* Then use the provided arc_eval.ipynb notebook to finalize and inspect your results.

Notes Copy Chapter

- Small-sample learning typically exhibits accuracy variance of around ±2 points.
- For Sudoku-Extreme (1,000-example dataset), late-stage overfitting may cause numerical instability during training and Q-learning. It is advisable to use early stopping once the training accuracy approaches 100%.

Citation 📜 Copy Chapter

@misc{wang2025hierarchicalreasoningmodel,
      title={Hierarchical Reasoning Model}, 
      author={Guan Wang and Jin Li and Yuhao Sun and Xing Chen and Changling Liu and Yue Wu and Meng Lu and Sen Song and Yasin Abbasi Yadkori},
      year={2025},
      eprint={2506.21734},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.21734}, 
}