--- 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 ---
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: ```bash git clone https://github.com/Thrillcrazyer/LoopUS.git cd LoopUS uv run chat.py ``` # Illustration of LoopUS