English(EN) Parallelizable Probabilistic Latent Reasoning for Diverse Reasoning Trajectories

新的PLRM框架实现了可并行化的概率潜在推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员推出了一种新颖的变分框架——概率潜在推理模型（PLRM），旨在提高语言模型推理轨迹的效率和多样性。与确定性潜在模型不同，PLRM能够捕获和建模多个有效推理路径的分布。该框架利用了类似JEPA的目标函数来实现可并行化的后验推理，并引入了一个具有混合先验的离散策略变量来保留不同的推理过程。PLRM在单轨迹数据集上表现出竞争力，并在多轨迹任务上超越了现有的随机基线，同时实现了更高效的训练。 AI

影响这项研究可能带来语言模型更高效、更多样化的推理能力，从而可能提高它们在复杂任务上的表现。

排序理由该集群包含一篇详细介绍新模型框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Seokhyun Lee, Seongjun Lee, Changhee Lee · 2026-10-22 04:00

Parallelizable Probabilistic Latent Reasoning for Diverse Reasoning Trajectories

arXiv:2610.29385v1 Announce Type: new Abstract: Language models relying on verbalized Chain-of-Thought (CoT) are constrained to token-by-token generation in the language space, where many tokens serve textual coherence rather than core reasoning. This entangles genuine difference…

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Parallelizable Probabilistic Latent Reasoning for Diverse Reasoning Trajectories

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