Add pipeline tag and improve documentation

README.md

@@ -1,14 +1,16 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen3-1.7B-Base
 datasets:
 - HuggingFaceFW/fineweb-edu
+license: apache-2.0
 model_name: Qwen3_1.7B_LoopUS
-base_model:
-- Qwen/Qwen3-1.7B-Base
 tags:
 - LoopUS
 - LoopedTransformers
+pipeline_tag: text-generation
 ---
+
 <div align="center">
 <h1>LoopUS: <br> Recasting Pretrained LLMs into Looped Latent Refinement Models</h1>
 </div>
@@ -26,26 +28,46 @@ 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>
 
+# Introduction
 
-# Abstract
+**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.
 
-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.
+# Quick Start
 
-# QuickStart
+To use this model, clone the official repository and run the provided scripts:
 
 ```bash
 git clone https://github.com/Thrillcrazyer/LoopUS.git
 cd LoopUS
-uv run chat.py
+# Install dependencies
+uv sync
+# Run the chat interface
+uv run chat.py --model-name Thrillcrazyer/Qwen3_1.7B_LoopUS
 ```
 
 # 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 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}, 
+}
+```