Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth
  Approach
Authors: name: Jonas Geiping, name: Sean McLeish, name: Neel Jain, name: John Kirchenbauer, name: Siddharth Singh, name: Brian R. Bartoldson, name: Bhavya Kailkhura, name: Abhinav Bhatele, name: Tom Goldstein

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.