LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models

TL;DR

LoopUS is a post-training framework that transforms pretrained LLMs into looped architectures to enhance reasoning performance efficiently, without retraining from scratch.

Contribution

It introduces a novel latent-refinement architecture with four core components, enabling stable, efficient looping in pretrained models without extensive retraining.

Findings

Improves reasoning performance without additional training.

Stabilizes latent looping against computational and representation issues.

Operates as a post-training conversion, saving computational resources.

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. For more details, see https://thrillcrazyer.github.io/LoopUS