Qwen3_1.7B_LoopUS

<div align="center"> <h1>LoopUS: <br> Recasting Pretrained LLMs into Looped Latent Refinement Models</h1> </div> <p align="center"> <a href="https://pnubaelab.github.io/"><b>BAELAB</b></a>, Pusan National University, Busan, Korea <br> <a href="https://aidoheekim.github.io/"><b>DOLAB</b></a>, Changwon National University, Changwon, Korea </p> <p align="center"> <a href="https://thrillcrazyer.github.io/" target="_blank"><strong>Taekhyun Park</strong></a>¹, <a href="https://yongzzai.com/" target="_blank"><strong>Yongjae Lee</strong></a>¹, <a href="https://aidoheekim.github.io/" target="_blank"><strong>Dohee Kim</strong></a>², <a href="https://pnubaelab.github.io/" target="_blank"><strong>Hyerim Bae</string></a>^1,&dagger; </p> <p align="center"> <a href="https://github.com/Thrillcrazyer/LoopUS"><b>🌟 Github</b></a> | <a href="https://thrillcrazyer.github.io/LoopUS"><b>🌐 Project Page</b></a> | <a href="https://arxiv.org/abs/2605.11011"><b>📄 Paper</b></a> </p>

Introduction

Looped Depth Up-Scaling (LoopUS) is a post-training framework that converts a standard pretrained LLM into a looped latent refinement model. Instead of extending output traces, LoopUS restructures the model into an encoder, a looped reasoning block, and a decoder, then performs iterative latent refinement in the hidden space. This approach enables test-time compute scaling and improves reasoning-oriented performance without requiring recurrent training from scratch.

Quick Start

To use this model, clone the official repository and run the provided scripts:

git clone https://github.com/Thrillcrazyer/LoopUS.git
cd LoopUS
# Install dependencies
uv sync
# Run the chat interface
uv run chat.py --model-name Thrillcrazyer/Qwen3_1.7B_LoopUS

Illustration of LoopUS

<div align="center"> <img src="https://raw.githubusercontent.com/Thrillcrazyer/LoopUS/main/assets/Framework.png" width="800"/> </div>

Citation

If you find LoopUS useful in your research, please cite the following paper:

@misc{park2026loopus,
      title={LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models}, 
      author={Taekhyun Park and Yongjae Lee and Dohee Kim and Hyerim Bae},
      year={2026},
      eprint={2605.11011},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.11011}, 
}