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Computer Science > Machine Learning

arXiv:2502.05171 (cs)
[Submitted on 7 Feb 2025]

Title:Scaling up Test-Time Compute with Latent Reasoning: A Recurrent
Depth Approach

Authors:Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer,
Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav
Bhatele, Tom Goldstein
View a PDF of the paper titled Scaling up Test-Time Compute with
Latent Reasoning: A Recurrent Depth Approach, by Jonas Geiping and 8
other authors
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    Abstract: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.

Comments: The model is available at this https URL. Code and data
          recipe can be found at this https URL
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as:  arXiv:2502.05171 [cs.LG]
          (or arXiv:2502.05171v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2502.05171
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jonas Geiping [view email]
[v1] Fri, 7 Feb 2025 18:55:02 UTC (12,678 KB)
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