LoopUS:
Recasting Pretrained LLMs into Looped Latent Refinement Models

BAELAB, Pusan National University, Busan, Korea
DOLAB, Changwon National University, Changwon, Korea

Taekhyun Park¹, Yongjae Lee¹, Dohee Kim², Hyerim Bae^1,†

🌟 Github | 🌐 Project Page | 📄 Paper

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

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}, 
}