The authors introduce LoopUS (Looped Depth Up-Scaling), a post-training method that converts a standard pretrained LLM into a looped architecture that iteratively refines hidden representations without generating longer output sequences. Instead of training a recurrent model from scratch or doing major architectural retrofits, LoopUS decomposes the pretrained model into an encoder, a looped reasoning block, and a decoder, using techniques like input-dependent selective gates (to prevent hidden-state drift), random deep supervision (for memory-efficient training over long loops), and a confidence head for adaptive early stopping. This lets you scale test-time compute through latent iteration while preserving pretrained capabilities.
Main takeaways:
- LoopUS converts a pretrained LLM into a looped architecture post-training, enabling iterative latent refinement without extending generated sequences
- Architecture: encoder → looped reasoning block → decoder, with selective gates to prevent drift and random deep supervision for efficient training
- Adaptive early exit via a confidence head allows variable compute based on problem difficulty
- Improves reasoning performance through test-time compute scaling without recurrent training from scratch or disrupting pretrained knowledge