Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Pretrains a depth-recurrent language model whose computation can be extended at inference time, separating test-time compute from generated token length.
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Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein
Advances in Neural Information Processing Systems, 2025, pp. 41340-41391.
DOI: 10.52202/085713-1380
Checked 2026-10-18 against the publication record. The published title, author list or year can differ from the preprint.
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Method comparisons
Recurrent depth and fixed points
Recurrent depth and fixed-point models repeat computation in different ways. Geiping and colleagues reuse a transformer block, allowing the number of passes to change at inference. The implicit state-space models studied by Schöne and colleagues instead iterate toward a fixed point, with experiments testing when approximate convergence is sufficient. Both add computation without adding a written reasoning trace, but their stopping rules and training costs differ.
Sources: Recurrent-depth paper · Implicit state-space paper
Paper details: Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach · Implicit Language Models are RNNs: Balancing Parallelization and Expressivity
Explore this approach
Recurrent reasoning — Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.
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