Explorations into the proposed recurrent hierarchical reasoning model by Wang et al. from Sapient Intelligence. Official repository is here
Update: HRM Analysis from Arc-AGI team
$ pip install HRM-pytorchimport torch
from HRM import HRM
hrm = HRM(
networks = [
dict(
dim = 32,
depth = 2,
attn_dim_head = 8,
heads = 1,
use_rmsnorm = True,
rotary_pos_emb = True,
pre_norm = False
),
dict(
dim = 32,
depth = 4,
attn_dim_head = 8,
heads = 1,
use_rmsnorm = True,
rotary_pos_emb = True,
pre_norm = False
)
],
num_tokens = 256,
dim = 32,
reasoning_steps = 10
)
seq = torch.randint(0, 256, (3, 1024))
labels = torch.randint(0, 256, (3, 1024))
loss, hiddens, _ = hrm(seq, labels = labels)
loss.backward()
loss, hiddens, _ = hrm(seq, hiddens = hiddens, labels = labels)
loss.backward()
# after much training
pred = hrm(seq, reasoning_steps = 5)@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},
}