Arip

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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.

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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Why does critic-free RL work for LLMs? This question has been in the air for a long time. To answer it, we propose the “Value-Gradient Hypothesis.” GRPO and PPO-style methods often improve reasoning without relying on a learned value model. Our paper’s central claim is that critic-free does not mean value-free: the actor’s backward pass can still carry a useful credit-assignment signal.

Why does critic-free RL work for LLMs? This question has been in the air for a long time. To answer it, we propose the “Value-Gradient Hypothesis.” GRPO and PPO-style methods often improve reasoning without relying on a learned value model. Our paper’s central claim is that critic-free does not mean value-free: the actor’s backward pass can still carry a useful credit-assignment signal.

42,486 görüntüleme • 2 ay önce

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