Improve model card: add pipeline tag, paper link, and code link

README.md

@@ -1,14 +1,16 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen3-4B-Base
 datasets:
 - HuggingFaceFW/fineweb-edu
+license: apache-2.0
 model_name: Qwen3_1.7B_LoopUS_SFT
-base_model:
-- Qwen/Qwen3-4B-Base
+pipeline_tag: text-generation
 tags:
 - LoopUS
 - LoopedTrasnformers
 ---
+
 <div align="center">
 <h1>LoopUS: <br> Recasting Pretrained LLMs into Looped Latent Refinement Models</h1>
 </div>
@@ -26,26 +28,41 @@ tags:
 </p>
 
 <p align="center">
-  <a href="https://thrillcrazyer.github.io/LoopUS"><b>🌟 Github</b></a> |
-  <a href="https://huggingface.co/Thrillcrazyer/Qwen3_1.7B_LoopUS"><b>📥 Download</b></a> |
+  <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>
 
-
 # Abstract
 
-Looped computation shows promise in improving the reasoning-oriented performance of LLMs by scaling test-time compute. However, existing approaches typically require either training recurrent models from scratch or applying disruptive retrofits, which involve substantial computational costs and may compromise pretrained capabilities. To address these limitations, we introduce \textbf{Looped Depth Up-Scaling} (LoopUS), a post-training framework that converts a standard pretrained LLM into a looped architecture. As a key technical contribution, LoopUS recasts the pretrained LLM into an encoder, a looped reasoning block, and a decoder. It operationalizes this latent-refinement architecture through four core components: (1) block decomposition, guided by staged representation dynamics; (2) an input-dependent selective gate to mitigate hidden-state drift; (3) random deep supervision for memory-efficient learning over long recursive horizons; and (4) a confidence head for adaptive early exiting. Collectively, these mechanisms transform a standard non-looped model into a looped form while stabilizing it against both computational bottlenecks and representation collapse. Through stable latent looping, LoopUS improves reasoning-oriented performance without extending the generated traces or requiring recurrent training from scratch.
+Looped computation shows promise in improving the reasoning-oriented performance of LLMs by scaling test-time compute. **Looped Depth Up-Scaling** (LoopUS) is 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 improves reasoning-oriented performance without extending the generated traces or requiring recurrent training from scratch.
 
 # QuickStart
 
+To use this model, clone the official repository and run the chat interface:
+
 ```bash
 git clone https://github.com/Thrillcrazyer/LoopUS.git
 cd LoopUS
-uv run chat.py
+uv sync
+uv run chat.py --model-name Thrillcrazyer/Qwen3_1.7B_LoopUS_SFT
 ```
 
 # Illustration of LoopUS
 
 <div align="center">
 <img src="https://raw.githubusercontent.com/Thrillcrazyer/LoopUS/main/assets/Framework.png" width="800"/>
-</div>
+</div>
+
+# Citation
+
+If you find LoopUS useful in your research, please cite:
+
+```bibtex
+@article{park2026loopus,
+  title={LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models},
+  author={Park, Taekhyun and Lee, Yongjae and Kim, Dohee and Bae, Hyerim},
+  journal={arXiv preprint arXiv:2605.11011},
+  year={2026}
+}
+```