English(EN) OpenESM: Energy-Steered Models Are Scalable Foundation Learners

OpenESM架构在大规模语言建模中优于GPTs

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员推出了一种新颖的大规模语言建模架构OpenESM，该架构将能量引导与令牌生成相结合。该方法在生成令牌之前，在能量景观上优化潜在状态，旨在改进生成过程。OpenESM在海量数据集上进行了预训练，展示了良好的可扩展性，并在26项评估的下游任务中的大多数任务上优于传统的生成式预训练变换器（GPTs）。研究人员已发布模型权重和训练基础设施，以促进进一步的研究和复现。 AI

影响引入了一种新架构，与现有的Transformer模型相比，可能提供更好的性能和可扩展性。

排序理由介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 8 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Yuan Pu, Yudong Lu, Ting Wang, Zhicong Hong, Qinuo Li, Xuansheng Wu, Na Zou, Xia Hu, Guanchu Wang · 2026-10-27 04:00

OpenESM：能量引导模型是可扩展的基础学习器

arXiv:2610.33005v1 Announce Type: new Abstract: Energy-based models have shown promising properties for domain-specific tasks but remain challenging in modeling general-purpose scenarios. To bridge this gap, we design energy-steered models (ESMs) for foundational language modelin…

本文如何被排名

报道来源 [1]

OpenESM：能量引导模型是可扩展的基础学习器

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