---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- arloop
- recurrent
- custom_code
- pretrained
---

# ARLoop-1.2B

📚 [Paper](https://arxiv.org/abs/2611.05234) · 💻 [Code](https://github.com/OSU-NLP-Group/ARLoop)

ARLoop is a fully-open pretrained looped transformer with attention residuals. This release
contains the checkpoints of the 1.2B, 500B-token training run.
It is a **base pretrained model**, intended for text completion, which can be used for mid-training and post-training.

![Figure 1: ARLoop benchmark results](assets/figure1.png)

## Load and generate

```bash
pip install 'transformers==5.12.1' safetensors accelerate
# Install the PyTorch build appropriate for your CUDA environment separately.
```

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "osunlp/ARLoop-1.2B"  # Or the local ARLOOP_hf directory.
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, dtype=torch.bfloat16,
).to("cuda").eval()
inputs = tokenizer("The capital of France is", return_tensors="pt").to("cuda")
with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

Weights load in BF16 for inference. The stored checkpoint retains the original FP32 weights; routing and normalization statistics use FP32 internally.

## Architecture

| Property | Value |
| --- | --- |
| Physical transformer layers | 24, shared across 3 recurrent passes |
| Effective transformer depth | 72 |
| Hidden / MLP intermediate width | 2048 / 5632 |
| Attention / KV heads | 16 / 4 |
| Context length | 4096 |
| RoPE base | 50000 |
| Attention-residual block size | 24 sublayers |
| Recent residual blocks | 3, with input embedding retained |
| Value embeddings | Shared across alternating physical layers |
| Tokenizer vocabulary | 49152, SmolLM2 tokenizer used in training |
| Output head | Tied to token embeddings |

## License

Model release: [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

## Training checkpoints

`main` contains the final checkpoint. Each revision below is independently loadable
with `revision="step-010000"` (use the same revision for the model and tokenizer), 
where 10000 denotes the number of optimization steps.

| Optimizer step | Training tokens | Revision |
| ---: | ---: | --- |
| 10,000 | 20,971,520,000 | [step-010000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-010000) |
| 20,000 | 41,943,040,000 | [step-020000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-020000) |
| 30,000 | 62,914,560,000 | [step-030000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-030000) |
| 40,000 | 83,886,080,000 | [step-040000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-040000) |
| 50,000 | 104,857,600,000 | [step-050000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-050000) |
| 60,000 | 125,829,120,000 | [step-060000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-060000) |
| 70,000 | 146,800,640,000 | [step-070000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-070000) |
| 80,000 | 167,772,160,000 | [step-080000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-080000) |
| 90,000 | 188,743,680,000 | [step-090000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-090000) |
| 100,000 | 209,715,200,000 | [step-100000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-100000) |
| 110,000 | 230,686,720,000 | [step-110000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-110000) |
| 120,000 | 251,658,240,000 | [step-120000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-120000) |
| 130,000 | 272,629,760,000 | [step-130000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-130000) |
| 140,000 | 293,601,280,000 | [step-140000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-140000) |
| 150,000 | 314,572,800,000 | [step-150000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-150000) |
| 160,000 | 335,544,320,000 | [step-160000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-160000) |
| 170,000 | 356,515,840,000 | [step-170000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-170000) |
| 180,000 | 377,487,360,000 | [step-180000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-180000) |
| 190,000 | 398,458,880,000 | [step-190000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-190000) |
| 200,000 | 419,430,400,000 | [step-200000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-200000) |
| 210,000 | 440,401,920,000 | [step-210000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-210000) |
| 220,000 | 461,373,440,000 | [step-220000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-220000) |
| 230,000 | 482,344,960,000 | [step-230000](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-230000) |
| 238,417 (final) | 499,996,688,384 | [main](https://huggingface.co/osunlp/ARLoop-1.2B/tree/main) / [step-238417](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-238417) |

All 23 scheduled intermediate checkpoints from 10k through 230k are available.

## Citation

```bibtex
@misc{yao2026arloop,
  title={{ARLoop}: Fully-Open Pre-trained Looped Transformers with Attention Residuals},
  author={Yuekun Yao and Yupei Du and Dawei Zhu and Huan Sun},
  year={2026},
  eprint={2611.05234},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2611.05234}
}
```