Leveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion

• URL: http://arxiv.org/abs/2605.04291v1
• Date: Tue, 05 May 2026 20:51:03 GMT
• Title: Leveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion
• Abstract summary: We present a discrete diffusion-based language model using Glauber dynamics from statistical physics.<n>Our main insight is that instead of trying to train a discrete state space diffusion model using Glauber dynamics with a uniform transition kernel as the forward process, one can set up an energy function'' based on pretrained causal/masked language models.
• Score: 4.990506857330584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a discrete diffusion-based language model using Glauber dynamics from statistical physics. Our main insight is that instead of trying to train a discrete state space diffusion model using Glauber dynamics with a uniform transition kernel as the forward process, one can set up an ``energy function'' based on pretrained causal/masked language models. When viewed as the stationary distribution, this energy function allows us to significantly improve the quality of the generated text. Incorporating UL2 as the pretrained model into our diffusion pipeline, we outperform prior diffusion based LMs and perform competitively with autoregressive models of comparable model sizes. Furthermore, our models are competitive with or outperform prior diffusion models and GPT-2 style auto-regressive models on zero-shot common sense reasoning tasks as well as planning and search tasks like Sudoku and Zebra puzzles.

Related papers

• Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics [1.6441639841508602]
  We formulate system identification as an in-context meta-learning problem.<n>We compare deterministic and generative sequence models for forward dynamics prediction.<n>Results highlight generative meta-models as a promising direction for robust system identification in robotics.
  arXiv  Detail & Related papers  (2026-04-15T00:21:49Z)
• Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models [50.77646970127369]
  We propose an energy-based diffusion model with a Fokker--Planck-derived regularization term to enforce consistency.<n>We demonstrate our approach by sampling and simulating multiple biomolecular systems, including fast-folding proteins.
  arXiv  Detail & Related papers  (2025-06-20T16:38:29Z)
• TESS 2: A Large-Scale Generalist Diffusion Language Model [24.91689676432666]
  TESS 2 is an instruction-following diffusion language model that outperforms contemporary instruction-tuned diffusion models.<n>We find that adaptation training as well as the choice of the base model is crucial for training good instruction-following diffusion models.<n>We propose reward guidance, a novel and modular inference-time guidance procedure to align model outputs without needing to train the underlying model.
  arXiv  Detail & Related papers  (2025-02-19T17:50:31Z)
• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step.<n>Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Heavy-Tailed Diffusion Models [38.713884992630675]
  We show that traditional diffusion and flow-matching models fail to capture heavy-tailed behavior. We address this by repurposing the diffusion framework for heavy-tail estimation. We introduce t-EDM and t-Flow, extensions of existing diffusion and flow models that employ a Student-t prior.
  arXiv  Detail & Related papers  (2024-10-18T04:29:46Z)
• Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models [100.53662473219806]
  Diffusion-of-Thought (DoT) is a novel approach that integrates diffusion models with Chain-of-Thought.<n>DoT allows reasoning steps to diffuse over time through a diffusion language model.<n>Our results demonstrate the effectiveness of DoT in multi-digit multiplication, logic, and grade school math problems.
  arXiv  Detail & Related papers  (2024-02-12T16:23:28Z)
• Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution [67.9215891673174]
  We propose score entropy as a novel loss that naturally extends score matching to discrete spaces. We test our Score Entropy Discrete Diffusion models on standard language modeling tasks.
  arXiv  Detail & Related papers  (2023-10-25T17:59:12Z)
• A Survey of Diffusion Models in Natural Language Processing [11.233768932957771]
  Diffusion models capture the diffusion of information or signals across a network or manifold. This paper discusses the different formulations of diffusion models used in NLP, their strengths and limitations, and their applications.
  arXiv  Detail & Related papers  (2023-05-24T03:25:32Z)
• DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models [81.84866217721361]
  DiffusionBERT is a new generative masked language model based on discrete diffusion models. We propose a new noise schedule for the forward diffusion process that controls the degree of noise added at each step. Experiments on unconditional text generation demonstrate that DiffusionBERT achieves significant improvement over existing diffusion models for text.
  arXiv  Detail & Related papers  (2022-11-28T03:25:49Z)
• How Much is Enough? A Study on Diffusion Times in Score-based Generative Models [76.76860707897413]
  Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution. We show how an auxiliary model can be used to bridge the gap between the ideal and the simulated forward dynamics, followed by a standard reverse diffusion process.
  arXiv  Detail & Related papers  (2022-06-10T15:09:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.