--- base_model: - Qwen/Qwen3-1.7B-Base datasets: - HuggingFaceFW/fineweb-edu license: apache-2.0 model_name: Qwen3_1.7B_LoopUS tags: - LoopUS - LoopedTransformers pipeline_tag: text-generation ---

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 Bae1,†

🌟 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: ```bash 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: ```bibtex @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}, } ```