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DeepLoop: Depth Scaling for Looped Transformers

Authors: Shuzhen Li, Yifan Zhang, Jiacheng Guo, Quanquan Gu, Mengdi Wang

DeepLoop is a residual-scaling rule for weight-tied looped Transformers. Looped models increase effective depth by revisiting the same physical blocks, and the DeepNorm exponent that stabilizes untied depth ($p=1/4$) is no longer the right one. DeepLoop makes the tied-depth effect explicit through a visit-alignment analysis and keeps the Post-LN DeepNorm architecture with a single loop-aware scaling rule:

$$ \mathbf{x}_{i+1} = \mathrm{Norm}\big(\alpha,\mathbf{x}_i + f(\mathbf{x}_i)\big), \qquad \alpha = (2N)^{1/2},\quad \beta = (8N)^{-1/2}, $$

where $N$ is the unrolled (effective) depth and $\beta$ is the initialization gain of each residual-branch output projection. The exponent $p=1/2$ is the aligned-visit threshold; it recovers DeepNorm's $1/4$ when visits decorrelate. The same rule applies to hierarchical recurrent reasoners (HRM), where it improves voted accuracy on ARC-AGI.

Repository structure

model/ model definitions (GPT-MHA-RoPE, looped variants, LoopDeepNorm scaling)
config/ training configurations (FineWeb-Edu LM and ARC-AGI)
utils/ tokenizers, optimizer, checkpoint, distributed, reproducibility utilities
scripts/ training / evaluation launchers
lib/runtime.sh shared venv/env setup
eval/ lm-evaluation-harness and ARC-AGI eval runners
integrations/
hrm/ DeepLoop applied to the Hierarchical Reasoning Model (ARC-AGI)
vanilla_hrm/ reproducible vanilla-HRM baseline package (build + eval)
train_adam_finewebedu.py FineWeb-Edu language-modeling entry point
train_adam_arc_agi.py ARC-AGI entry point
dataloader.py, arc_agi_dataloader.py token-stream / ARC data loaders

Installation

python -m venv .venv &&source .venv/bin/activate
pip install -r requirements.txt
# or: pip install -e .

Usage

Language modeling (FineWeb-Edu)

Multi-GPU training via torchrun; each experiment is a config file:

torchrun --standalone --nproc_per_node=4 \
train_adam_finewebedu.py config/train_gpt_mha_rope_small_loop3_adam_50BT_ctx1024_80g4.py

The base baseline keeps $\alpha=\beta=1$; the DeepLoop variant enables the $\alpha=(2N)^{1/2},\ \beta=(8N)^{-1/2}$ scaling on every block. Loop count $R$ is set in the config.

Reasoning (ARC-AGI via HRM)

integrations/hrm/ provides the DeepLoop residual-scaling drop-in for the Hierarchical Reasoning Model (hrm_act_v1_dl.py + hrm_v1_dl*.yaml) and the p-sweep launchers. integrations/vanilla_hrm/ provides a hash-verified vanilla-HRM baseline package (dataset rebuild + evaluation). See the README in each directory for step-by-step instructions.

Citation

@article{deeploop2026,
title = {DeepLoop: Depth Scaling for Looped Transformers},
author = {Li, Shuzhen and Zhang, Yifan and Guo, Jiacheng and Gu, Quanquan and Wang, Mengdi},
year = {2026}
}

License

Apache License 2.0. See LICENSE.

About

No description, website, or topics provided.

Resources

Readme
Apache-2.0 license
Activity

Stars

33 stars

Watchers

1 watching

Forks

1 fork
Report repository

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Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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DeepLoop: Depth Scaling for Looped Transformers
Authors: Shuzhen Li, Yifan Zhang, Jiacheng Guo, Quanquan Gu, Mengdi Wang
DeepLoop is a residual-scaling rule for weight-tied looped Transformers.
Looped models increase effective depth by revisiting the same physical blocks,
and the DeepNorm exponent that stabilizes untied depth ($p=1/4$) is no longer
the right one. DeepLoop makes the tied-depth effect explicit through a
visit-alignment analysis and keeps the Post-LN DeepNorm architecture with a
single loop-aware scaling rule:
$$
\mathbf{x}_{i+1} = \mathrm{Norm}\big(\alpha,\mathbf{x}_i + f(\mathbf{x}_i)\big),
\qquad \alpha = (2N)^{1/2},\quad \beta = (8N)^{-1/2},
$$
where $N$ is the unrolled (effective) depth and $\beta$ is the initialization
gain of each residual-branch output projection. The exponent $p=1/2$ is the
aligned-visit threshold; it recovers DeepNorm's $1/4$ when visits decorrelate.
The same rule applies to hierarchical recurrent reasoners (HRM), where it
improves voted accuracy on ARC-AGI.
Repository structure
model/ model definitions (GPT-MHA-RoPE, looped variants, LoopDeepNorm scaling)
config/ training configurations (FineWeb-Edu LM and ARC-AGI)
utils/ tokenizers, optimizer, checkpoint, distributed, reproducibility utilities
scripts/ training / evaluation launchers
lib/runtime.sh shared venv/env setup
eval/ lm-evaluation-harness and ARC-AGI eval runners
integrations/
hrm/ DeepLoop applied to the Hierarchical Reasoning Model (ARC-AGI)
vanilla_hrm/ reproducible vanilla-HRM baseline package (build + eval)
train_adam_finewebedu.py FineWeb-Edu language-modeling entry point
train_adam_arc_agi.py ARC-AGI entry point
dataloader.py, arc_agi_dataloader.py token-stream / ARC data loaders

Installation
python -m venv .venv &&source .venv/bin/activate
pip install -r requirements.txt
# or: pip install -e .
Usage
Language modeling (FineWeb-Edu)
Multi-GPU training via torchrun; each experiment is a config file:
torchrun --standalone --nproc_per_node=4 \
train_adam_finewebedu.py config/train_gpt_mha_rope_small_loop3_adam_50BT_ctx1024_80g4.py
The base baseline keeps $\alpha=\beta=1$; the DeepLoop variant enables the
$\alpha=(2N)^{1/2},\ \beta=(8N)^{-1/2}$ scaling on every block. Loop count $R$
is set in the config.
Reasoning (ARC-AGI via HRM)
integrations/hrm/ provides the DeepLoop residual-scaling drop-in for the
Hierarchical Reasoning Model
(hrm_act_v1_dl.py + hrm_v1_dl*.yaml) and the p-sweep launchers.
integrations/vanilla_hrm/ provides a hash-verified vanilla-HRM baseline
package (dataset rebuild + evaluation). See the README in each directory for
step-by-step instructions.
Citation
@article{deeploop2026,
title = {DeepLoop: Depth Scaling for Looped Transformers},
author = {Li, Shuzhen and Zhang, Yifan and Guo, Jiacheng and Gu, Quanquan and Wang, Mengdi},
year = {2026}
}
License
Apache License 2.0. See LICENSE.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Stars
33 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
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var __m = "*";
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DeepLoop: Depth Scaling for Looped Transformers
Authors: Shuzhen Li, Yifan Zhang, Jiacheng Guo, Quanquan Gu, Mengdi Wang
DeepLoop is a residual-scaling rule for weight-tied looped Transformers.
Looped models increase effective depth by revisiting the same physical blocks,
and the DeepNorm exponent that stabilizes untied depth ($p=1/4$) is no longer
the right one. DeepLoop makes the tied-depth effect explicit through a
visit-alignment analysis and keeps the Post-LN DeepNorm architecture with a
single loop-aware scaling rule:
$$
\mathbf{x}_{i+1} = \mathrm{Norm}\big(\alpha,\mathbf{x}_i + f(\mathbf{x}_i)\big),
\qquad \alpha = (2N)^{1/2},\quad \beta = (8N)^{-1/2},
$$
where $N$ is the unrolled (effective) depth and $\beta$ is the initialization
gain of each residual-branch output projection. The exponent $p=1/2$ is the
aligned-visit threshold; it recovers DeepNorm's $1/4$ when visits decorrelate.
The same rule applies to hierarchical recurrent reasoners (HRM), where it
improves voted accuracy on ARC-AGI.
Repository structure
model/ model definitions (GPT-MHA-RoPE, looped variants, LoopDeepNorm scaling)
config/ training configurations (FineWeb-Edu LM and ARC-AGI)
utils/ tokenizers, optimizer, checkpoint, distributed, reproducibility utilities
scripts/ training / evaluation launchers
lib/runtime.sh shared venv/env setup
eval/ lm-evaluation-harness and ARC-AGI eval runners
integrations/
hrm/ DeepLoop applied to the Hierarchical Reasoning Model (ARC-AGI)
vanilla_hrm/ reproducible vanilla-HRM baseline package (build + eval)
train_adam_finewebedu.py FineWeb-Edu language-modeling entry point
train_adam_arc_agi.py ARC-AGI entry point
dataloader.py, arc_agi_dataloader.py token-stream / ARC data loaders

Installation
python -m venv .venv &&source .venv/bin/activate
pip install -r requirements.txt
# or: pip install -e .
Usage
Language modeling (FineWeb-Edu)
Multi-GPU training via torchrun; each experiment is a config file:
torchrun --standalone --nproc_per_node=4 \
train_adam_finewebedu.py config/train_gpt_mha_rope_small_loop3_adam_50BT_ctx1024_80g4.py
The base baseline keeps $\alpha=\beta=1$; the DeepLoop variant enables the
$\alpha=(2N)^{1/2},\ \beta=(8N)^{-1/2}$ scaling on every block. Loop count $R$
is set in the config.
Reasoning (ARC-AGI via HRM)
integrations/hrm/ provides the DeepLoop residual-scaling drop-in for the
Hierarchical Reasoning Model
(hrm_act_v1_dl.py + hrm_v1_dl*.yaml) and the p-sweep launchers.
integrations/vanilla_hrm/ provides a hash-verified vanilla-HRM baseline
package (dataset rebuild + evaluation). See the README in each directory for
step-by-step instructions.
Citation
@article{deeploop2026,
title = {DeepLoop: Depth Scaling for Looped Transformers},
author = {Li, Shuzhen and Zhang, Yifan and Guo, Jiacheng and Gu, Quanquan and Wang, Mengdi},
year = {2026}
}
License
Apache License 2.0. See LICENSE.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Stars
33 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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DeepLoop: Depth Scaling for Looped Transformers
Authors: Shuzhen Li, Yifan Zhang, Jiacheng Guo, Quanquan Gu, Mengdi Wang
DeepLoop is a residual-scaling rule for weight-tied looped Transformers.
Looped models increase effective depth by revisiting the same physical blocks,
and the DeepNorm exponent that stabilizes untied depth ($p=1/4$) is no longer
the right one. DeepLoop makes the tied-depth effect explicit through a
visit-alignment analysis and keeps the Post-LN DeepNorm architecture with a
single loop-aware scaling rule:
$$
\mathbf{x}_{i+1} = \mathrm{Norm}\big(\alpha,\mathbf{x}_i + f(\mathbf{x}_i)\big),
\qquad \alpha = (2N)^{1/2},\quad \beta = (8N)^{-1/2},
$$
where $N$ is the unrolled (effective) depth and $\beta$ is the initialization
gain of each residual-branch output projection. The exponent $p=1/2$ is the
aligned-visit threshold; it recovers DeepNorm's $1/4$ when visits decorrelate.
The same rule applies to hierarchical recurrent reasoners (HRM), where it
improves voted accuracy on ARC-AGI.
Repository structure
model/ model definitions (GPT-MHA-RoPE, looped variants, LoopDeepNorm scaling)
config/ training configurations (FineWeb-Edu LM and ARC-AGI)
utils/ tokenizers, optimizer, checkpoint, distributed, reproducibility utilities
scripts/ training / evaluation launchers
lib/runtime.sh shared venv/env setup
eval/ lm-evaluation-harness and ARC-AGI eval runners
integrations/
hrm/ DeepLoop applied to the Hierarchical Reasoning Model (ARC-AGI)
vanilla_hrm/ reproducible vanilla-HRM baseline package (build + eval)
train_adam_finewebedu.py FineWeb-Edu language-modeling entry point
train_adam_arc_agi.py ARC-AGI entry point
dataloader.py, arc_agi_dataloader.py token-stream / ARC data loaders

Installation
python -m venv .venv &&source .venv/bin/activate
pip install -r requirements.txt
# or: pip install -e .
Usage
Language modeling (FineWeb-Edu)
Multi-GPU training via torchrun; each experiment is a config file:
torchrun --standalone --nproc_per_node=4 \
train_adam_finewebedu.py config/train_gpt_mha_rope_small_loop3_adam_50BT_ctx1024_80g4.py
The base baseline keeps $\alpha=\beta=1$; the DeepLoop variant enables the
$\alpha=(2N)^{1/2},\ \beta=(8N)^{-1/2}$ scaling on every block. Loop count $R$
is set in the config.
Reasoning (ARC-AGI via HRM)
integrations/hrm/ provides the DeepLoop residual-scaling drop-in for the
Hierarchical Reasoning Model
(hrm_act_v1_dl.py + hrm_v1_dl*.yaml) and the p-sweep launchers.
integrations/vanilla_hrm/ provides a hash-verified vanilla-HRM baseline
package (dataset rebuild + evaluation). See the README in each directory for
step-by-step instructions.
Citation
@article{deeploop2026,
title = {DeepLoop: Depth Scaling for Looped Transformers},
author = {Li, Shuzhen and Zhang, Yifan and Guo, Jiacheng and Gu, Quanquan and Wang, Mengdi},
year = {2026}
}
License
Apache License 2.0. See LICENSE.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Stars
33 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
DeepLoop: Depth Scaling for Looped Transformers
Authors: Shuzhen Li, Yifan Zhang, Jiacheng Guo, Quanquan Gu, Mengdi Wang
DeepLoop is a residual-scaling rule for weight-tied looped Transformers.
Looped models increase effective depth by revisiting the same physical blocks,
and the DeepNorm exponent that stabilizes untied depth ($p=1/4$) is no longer
the right one. DeepLoop makes the tied-depth effect explicit through a
visit-alignment analysis and keeps the Post-LN DeepNorm architecture with a
single loop-aware scaling rule:
$$
\mathbf{x}_{i+1} = \mathrm{Norm}\big(\alpha,\mathbf{x}_i + f(\mathbf{x}_i)\big),
\qquad \alpha = (2N)^{1/2},\quad \beta = (8N)^{-1/2},
$$
where $N$ is the unrolled (effective) depth and $\beta$ is the initialization
gain of each residual-branch output projection. The exponent $p=1/2$ is the
aligned-visit threshold; it recovers DeepNorm's $1/4$ when visits decorrelate.
The same rule applies to hierarchical recurrent reasoners (HRM), where it
improves voted accuracy on ARC-AGI.
Repository structure
model/ model definitions (GPT-MHA-RoPE, looped variants, LoopDeepNorm scaling)
config/ training configurations (FineWeb-Edu LM and ARC-AGI)
utils/ tokenizers, optimizer, checkpoint, distributed, reproducibility utilities
scripts/ training / evaluation launchers
lib/runtime.sh shared venv/env setup
eval/ lm-evaluation-harness and ARC-AGI eval runners
integrations/
hrm/ DeepLoop applied to the Hierarchical Reasoning Model (ARC-AGI)
vanilla_hrm/ reproducible vanilla-HRM baseline package (build + eval)
train_adam_finewebedu.py FineWeb-Edu language-modeling entry point
train_adam_arc_agi.py ARC-AGI entry point
dataloader.py, arc_agi_dataloader.py token-stream / ARC data loaders

Installation
python -m venv .venv &&source .venv/bin/activate
pip install -r requirements.txt
# or: pip install -e .
Usage
Language modeling (FineWeb-Edu)
Multi-GPU training via torchrun; each experiment is a config file:
torchrun --standalone --nproc_per_node=4 \
train_adam_finewebedu.py config/train_gpt_mha_rope_small_loop3_adam_50BT_ctx1024_80g4.py
The base baseline keeps $\alpha=\beta=1$; the DeepLoop variant enables the
$\alpha=(2N)^{1/2},\ \beta=(8N)^{-1/2}$ scaling on every block. Loop count $R$
is set in the config.
Reasoning (ARC-AGI via HRM)
integrations/hrm/ provides the DeepLoop residual-scaling drop-in for the
Hierarchical Reasoning Model
(hrm_act_v1_dl.py + hrm_v1_dl*.yaml) and the p-sweep launchers.
integrations/vanilla_hrm/ provides a hash-verified vanilla-HRM baseline
package (dataset rebuild + evaluation). See the README in each directory for
step-by-step instructions.
Citation
@article{deeploop2026,
title = {DeepLoop: Depth Scaling for Looped Transformers},
author = {Li, Shuzhen and Zhang, Yifan and Guo, Jiacheng and Gu, Quanquan and Wang, Mengdi},
year = {2026}
}
License
Apache License 2.0. See LICENSE.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Stars
33 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DeepLoop: Depth Scaling for Looped Transformers
Authors: Shuzhen Li, Yifan Zhang, Jiacheng Guo, Quanquan Gu, Mengdi Wang
DeepLoop is a residual-scaling rule for weight-tied looped Transformers.
Looped models increase effective depth by revisiting the same physical blocks,
and the DeepNorm exponent that stabilizes untied depth ($p=1/4$) is no longer
the right one. DeepLoop makes the tied-depth effect explicit through a
visit-alignment analysis and keeps the Post-LN DeepNorm architecture with a
single loop-aware scaling rule:
$$
\mathbf{x}_{i+1} = \mathrm{Norm}\big(\alpha,\mathbf{x}_i + f(\mathbf{x}_i)\big),
\qquad \alpha = (2N)^{1/2},\quad \beta = (8N)^{-1/2},
$$
where $N$ is the unrolled (effective) depth and $\beta$ is the initialization
gain of each residual-branch output projection. The exponent $p=1/2$ is the
aligned-visit threshold; it recovers DeepNorm's $1/4$ when visits decorrelate.
The same rule applies to hierarchical recurrent reasoners (HRM), where it
improves voted accuracy on ARC-AGI.
Repository structure
model/ model definitions (GPT-MHA-RoPE, looped variants, LoopDeepNorm scaling)
config/ training configurations (FineWeb-Edu LM and ARC-AGI)
utils/ tokenizers, optimizer, checkpoint, distributed, reproducibility utilities
scripts/ training / evaluation launchers
lib/runtime.sh shared venv/env setup
eval/ lm-evaluation-harness and ARC-AGI eval runners
integrations/
hrm/ DeepLoop applied to the Hierarchical Reasoning Model (ARC-AGI)
vanilla_hrm/ reproducible vanilla-HRM baseline package (build + eval)
train_adam_finewebedu.py FineWeb-Edu language-modeling entry point
train_adam_arc_agi.py ARC-AGI entry point
dataloader.py, arc_agi_dataloader.py token-stream / ARC data loaders

Installation
python -m venv .venv &&source .venv/bin/activate
pip install -r requirements.txt
# or: pip install -e .
Usage
Language modeling (FineWeb-Edu)
Multi-GPU training via torchrun; each experiment is a config file:
torchrun --standalone --nproc_per_node=4 \
train_adam_finewebedu.py config/train_gpt_mha_rope_small_loop3_adam_50BT_ctx1024_80g4.py
The base baseline keeps $\alpha=\beta=1$; the DeepLoop variant enables the
$\alpha=(2N)^{1/2},\ \beta=(8N)^{-1/2}$ scaling on every block. Loop count $R$
is set in the config.
Reasoning (ARC-AGI via HRM)
integrations/hrm/ provides the DeepLoop residual-scaling drop-in for the
Hierarchical Reasoning Model
(hrm_act_v1_dl.py + hrm_v1_dl*.yaml) and the p-sweep launchers.
integrations/vanilla_hrm/ provides a hash-verified vanilla-HRM baseline
package (dataset rebuild + evaluation). See the README in each directory for
step-by-step instructions.
Citation
@article{deeploop2026,
title = {DeepLoop: Depth Scaling for Looped Transformers},
author = {Li, Shuzhen and Zhang, Yifan and Guo, Jiacheng and Gu, Quanquan and Wang, Mengdi},
year = {2026}
}
License
Apache License 2.0. See LICENSE.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Stars
33 stars
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1 watching
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1 fork
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DeepLoop: Depth Scaling for Looped Transformers
Authors: Shuzhen Li, Yifan Zhang, Jiacheng Guo, Quanquan Gu, Mengdi Wang
DeepLoop is a residual-scaling rule for weight-tied looped Transformers.
Looped models increase effective depth by revisiting the same physical blocks,
and the DeepNorm exponent that stabilizes untied depth ($p=1/4$) is no longer
the right one. DeepLoop makes the tied-depth effect explicit through a
visit-alignment analysis and keeps the Post-LN DeepNorm architecture with a
single loop-aware scaling rule:
$$
\mathbf{x}_{i+1} = \mathrm{Norm}\big(\alpha,\mathbf{x}_i + f(\mathbf{x}_i)\big),
\qquad \alpha = (2N)^{1/2},\quad \beta = (8N)^{-1/2},
$$
where $N$ is the unrolled (effective) depth and $\beta$ is the initialization
gain of each residual-branch output projection. The exponent $p=1/2$ is the
aligned-visit threshold; it recovers DeepNorm's $1/4$ when visits decorrelate.
The same rule applies to hierarchical recurrent reasoners (HRM), where it
improves voted accuracy on ARC-AGI.
Repository structure
model/ model definitions (GPT-MHA-RoPE, looped variants, LoopDeepNorm scaling)
config/ training configurations (FineWeb-Edu LM and ARC-AGI)
utils/ tokenizers, optimizer, checkpoint, distributed, reproducibility utilities
scripts/ training / evaluation launchers
lib/runtime.sh shared venv/env setup
eval/ lm-evaluation-harness and ARC-AGI eval runners
integrations/
hrm/ DeepLoop applied to the Hierarchical Reasoning Model (ARC-AGI)
vanilla_hrm/ reproducible vanilla-HRM baseline package (build + eval)
train_adam_finewebedu.py FineWeb-Edu language-modeling entry point
train_adam_arc_agi.py ARC-AGI entry point
dataloader.py, arc_agi_dataloader.py token-stream / ARC data loaders

Installation
python -m venv .venv &&source .venv/bin/activate
pip install -r requirements.txt
# or: pip install -e .
Usage
Language modeling (FineWeb-Edu)
Multi-GPU training via torchrun; each experiment is a config file:
torchrun --standalone --nproc_per_node=4 \
train_adam_finewebedu.py config/train_gpt_mha_rope_small_loop3_adam_50BT_ctx1024_80g4.py
The base baseline keeps $\alpha=\beta=1$; the DeepLoop variant enables the
$\alpha=(2N)^{1/2},\ \beta=(8N)^{-1/2}$ scaling on every block. Loop count $R$
is set in the config.
Reasoning (ARC-AGI via HRM)
integrations/hrm/ provides the DeepLoop residual-scaling drop-in for the
Hierarchical Reasoning Model
(hrm_act_v1_dl.py + hrm_v1_dl*.yaml) and the p-sweep launchers.
integrations/vanilla_hrm/ provides a hash-verified vanilla-HRM baseline
package (dataset rebuild + evaluation). See the README in each directory for
step-by-step instructions.
Citation
@article{deeploop2026,
title = {DeepLoop: Depth Scaling for Looped Transformers},
author = {Li, Shuzhen and Zhang, Yifan and Guo, Jiacheng and Gu, Quanquan and Wang, Mengdi},
year = {2026}
}
License
Apache License 2.0. See LICENSE.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Stars
33 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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DeepLoop: Depth Scaling for Looped Transformers
Authors: Shuzhen Li, Yifan Zhang, Jiacheng Guo, Quanquan Gu, Mengdi Wang
DeepLoop is a residual-scaling rule for weight-tied looped Transformers.
Looped models increase effective depth by revisiting the same physical blocks,
and the DeepNorm exponent that stabilizes untied depth ($p=1/4$) is no longer
the right one. DeepLoop makes the tied-depth effect explicit through a
visit-alignment analysis and keeps the Post-LN DeepNorm architecture with a
single loop-aware scaling rule:
$$
\mathbf{x}_{i+1} = \mathrm{Norm}\big(\alpha,\mathbf{x}_i + f(\mathbf{x}_i)\big),
\qquad \alpha = (2N)^{1/2},\quad \beta = (8N)^{-1/2},
$$
where $N$ is the unrolled (effective) depth and $\beta$ is the initialization
gain of each residual-branch output projection. The exponent $p=1/2$ is the
aligned-visit threshold; it recovers DeepNorm's $1/4$ when visits decorrelate.
The same rule applies to hierarchical recurrent reasoners (HRM), where it
improves voted accuracy on ARC-AGI.
Repository structure
model/ model definitions (GPT-MHA-RoPE, looped variants, LoopDeepNorm scaling)
config/ training configurations (FineWeb-Edu LM and ARC-AGI)
utils/ tokenizers, optimizer, checkpoint, distributed, reproducibility utilities
scripts/ training / evaluation launchers
lib/runtime.sh shared venv/env setup
eval/ lm-evaluation-harness and ARC-AGI eval runners
integrations/
hrm/ DeepLoop applied to the Hierarchical Reasoning Model (ARC-AGI)
vanilla_hrm/ reproducible vanilla-HRM baseline package (build + eval)
train_adam_finewebedu.py FineWeb-Edu language-modeling entry point
train_adam_arc_agi.py ARC-AGI entry point
dataloader.py, arc_agi_dataloader.py token-stream / ARC data loaders

Installation
python -m venv .venv &&source .venv/bin/activate
pip install -r requirements.txt
# or: pip install -e .
Usage
Language modeling (FineWeb-Edu)
Multi-GPU training via torchrun; each experiment is a config file:
torchrun --standalone --nproc_per_node=4 \
train_adam_finewebedu.py config/train_gpt_mha_rope_small_loop3_adam_50BT_ctx1024_80g4.py
The base baseline keeps $\alpha=\beta=1$; the DeepLoop variant enables the
$\alpha=(2N)^{1/2},\ \beta=(8N)^{-1/2}$ scaling on every block. Loop count $R$
is set in the config.
Reasoning (ARC-AGI via HRM)
integrations/hrm/ provides the DeepLoop residual-scaling drop-in for the
Hierarchical Reasoning Model
(hrm_act_v1_dl.py + hrm_v1_dl*.yaml) and the p-sweep launchers.
integrations/vanilla_hrm/ provides a hash-verified vanilla-HRM baseline
package (dataset rebuild + evaluation). See the README in each directory for
step-by-step instructions.
Citation
@article{deeploop2026,
title = {DeepLoop: Depth Scaling for Looped Transformers},
author = {Li, Shuzhen and Zhang, Yifan and Guo, Jiacheng and Gu, Quanquan and Wang, Mengdi},
year = {2026}
}
License
Apache License 2.0. See LICENSE.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Stars
33 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages