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

Abstract

Looped computation shows promise in improving the reasoning-oriented performance of LLMs by scaling test-time compute. We introduce Looped Depth Up-Scaling (LoopUS), a post-training framework that converts a standard pretrained LLM into a looped architecture. LoopUS recasts the pretrained LLM into an encoder, a looped reasoning block, and a decoder. It operationalizes this latent-refinement architecture through block decomposition, an input-dependent selective gate, random deep supervision, and a confidence head for adaptive early exiting. Through stable latent looping, LoopUS improves reasoning-oriented performance without extending the generated traces or requiring recurrent training from scratch.

QuickStart

To use LoopUS, clone the repository and run the chat script:

git clone https://github.com/Thrillcrazyer/LoopUS.git cd LoopUS uv run chat.py

Illustration of LoopUS

Citation

If you find this work useful, please cite:

@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}, }