[Submitted on 7 Oct 2026]
Title:OpenESM: Energy-Steered Models Are Scalable Foundation Learners
View a PDF of the paper titled OpenESM: Energy-Steered Models Are Scalable Foundation Learners, by Yuan Pu and Yudong Lu and Ting Wang and Zhicong Hong and Qinuo Li and Xuansheng Wu and Na Zou and Xia Hu and Guanchu Wang
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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 modeling. ESMs integrate energy steering and token generation within a single architecture to leverage the energy-guided search to refine token generation. Specifically, ESMs first optimize a latent state over the energy landscape, then leverages the refined latent state for token generation. By performing such energy-guided pre-training on large-scale corpora, ESMs learn generally effective energy landscape where favorable tokens correspond to lower energy values. To explore the scaling behavior of ESMs, we pre-train ESMs on three large-scale corpora, then evaluate across 26 downstream tasks spanning a broad range of domains. Our results demonstrate that ESMs have promising scaling behaviors with increasing model size and training data, and outperform GPTs on most downstream tasks. These findings highlight ESMs as a promising architecture for large-scale pre-training and general purpose problem solving. We fully release the model weights and training infrastructure to enable the community to completely reproduce and extend our results.
GitHub: this https URL HuggingFace: this https URL
Comments: 12 pages, 7 figures, 3 tables
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2610.33005 [cs.CL]
(or arXiv:2610.33005v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.33005
arXiv-issued DOI via DataCite
Submission history
From: Guanchu Wang [view email] [v1] Wed, 7 Oct 2026 13:19:02 UTC (5,625 KB)
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