We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent block, thereby unrolling to arbitrary depth at test-time. This stands in contrast to mainstream reasoning models that scale up compute by producing more tokens. Unlike approaches based on chain-of-thought, our approach does not require any specialized training data, can work with small context windows, and can capture types of reasoning that are not easily represented in words. We scale a proof-of-concept model to 3.5 billion parameters and 800 billion tokens. We show that the resulting model can improve its performance on reasoning benchmarks, sometimes dramatically, up to a computation load equivalent to 50 billion parameters.
@article{arxiv.2502.05171,
title = {Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach},
author = {Jonas Geiping and Sean McLeish and Neel Jain and John Kirchenbauer and Siddharth Singh and Brian R. Bartoldson and Bhavya Kailkhura and Abhinav Bhatele and Tom Goldstein},
journal= {arXiv preprint arXiv:2502.05171},
year = {2025}
}
Comments
The model is available at https://huggingface.co/tomg-group-umd/huginn-0125. Code and data recipe can be found at https://github.com/seal-rg/recurrent-pretraining