base_model:
- TinyLlama/TinyLlama_v1.1
datasets:
- HuggingFaceFW/fineweb-edu
license: apache-2.0
model_name: TinyLlama_v1.1_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
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},
}