@machinestein: ICML 2026: Latent Reasoning in TRMs is Secretly a Policy Improvement Operator Why does recursive reasoning, especially …
Summary
The paper reveals that latent reasoning in transformer-based reasoning models (TRMs) functions as a policy improvement operator, and proposes an algorithm that enhances learning and inference efficiency by up to 18x.
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Cached at: 06/16/26, 05:40 PM
ICML 2026: Latent Reasoning in TRMs is Secretly a Policy Improvement Operator
Why does recursive reasoning, especially latent reasoning, actually work? The theory is still young, and even mechanistic explanations are limited.
We close part of this gap by showing that latent reasoning is secretly doing policy improvement. Each recursion pushes the model steadily toward the target.
Based on this view, we propose an algorithm that boosts learning and inference efficiency by up to 18x.
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