‘discrete diffusion model’ directory
- See Also
-
Links
- “Introducing Mercury, the First Commercial-Scale Diffusion Large Language Model: We Trained Diffusion Large Language Models That Are up to 10× Faster & Cheaper Than Current LLMs, Pushing the Frontier of Intelligence & Speed for Language Models ”, Labs 2025
- “LLaDA: Large Language Diffusion Models ”, Nie et al 2025
- “Scaling up Masked Diffusion Models on Text ”, Nie et al 2024
- “Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning ”, Ye et al 2024
- “RADD: Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data ”, Ou et al 2024
- “Simplified and Generalized Masked Diffusion for Discrete Data ”, Shi et al 2024
- “Σ-GPTs: A New Approach to Autoregressive Models ”, Pannatier et al 2024
- “CLLMs: Consistency Large Language Models ”, Kou et al 2024
- “Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion ”, Zhang et al 2023
- “CodeFusion: A Pre-Trained Diffusion Model for Code Generation ”, Singh et al 2023
- “SEDD: Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution ”, Lou et al 2023
- “Bayesian Flow Networks ”, Graves et al 2023
- “Likelihood-Based Diffusion Language Models ”, Gulrajani & Hashimoto 2023
- “Accelerating Transformer Inference for Translation via Parallel Decoding ”, Santilli et al 2023
- “Difformer: Empowering Diffusion Model on Embedding Space for Text Generation ”, Gao et al 2022
- “Score-Based Continuous-Time Discrete Diffusion Models ”, Sun et al 2022
- “CDCD: Continuous Diffusion for Categorical Data ”, Dieleman et al 2022
- “Self-Conditioned Embedding Diffusion for Text Generation ”, Strudel et al 2022
- “DiffusER: Discrete Diffusion via Edit-Based Reconstruction ”, Reid et al 2022
- “DiffuSeq: Sequence to Sequence Text Generation With Diffusion Models ”, Gong et al 2022
- “Analog Bits: Generating Discrete Data Using Diffusion Models With Self-Conditioning ”, Chen et al 2022
- “Diffusion-LM Improves Controllable Text Generation ”, Li et al 2022
- “Time Control: Language Modeling via Stochastic Processes ”, Wang et al 2022
- “Step-Unrolled Denoising Autoencoders for Text Generation ”, Savinov et al 2021
- “Zero-Shot Translation Using Diffusion Models ”, Nachmani & Dovrat 2021
- “Autoregressive Diffusion Models ”, Hoogeboom et al 2021
- “Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models ”, Johnson et al 2021
- “Structured Denoising Diffusion Models in Discrete State-Spaces ”, Austin et al 2021
- “Symbolic Music Generation With Diffusion Models ”, Mittal et al 2021
- “Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions ”, Hoogeboom et al 2021
- Sort By Magic
- Miscellaneous
- Bibliography
See Also
Links
“Introducing Mercury, the First Commercial-Scale Diffusion Large Language Model: We Trained Diffusion Large Language Models That Are up to 10× Faster & Cheaper Than Current LLMs, Pushing the Frontier of Intelligence & Speed for Language Models ”, Labs 2025
“LLaDA: Large Language Diffusion Models ”, Nie et al 2025
“Scaling up Masked Diffusion Models on Text ”, Nie et al 2024
“Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning ”, Ye et al 2024
Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning
“RADD: Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data ”, Ou et al 2024
RADD: Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
“Simplified and Generalized Masked Diffusion for Discrete Data ”, Shi et al 2024
Simplified and Generalized Masked Diffusion for Discrete Data
“Σ-GPTs: A New Approach to Autoregressive Models ”, Pannatier et al 2024
“CLLMs: Consistency Large Language Models ”, Kou et al 2024
“Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion ”, Zhang et al 2023
Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
“CodeFusion: A Pre-Trained Diffusion Model for Code Generation ”, Singh et al 2023
CodeFusion: A Pre-trained Diffusion Model for Code Generation
“SEDD: Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution ”, Lou et al 2023
SEDD: Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
“Bayesian Flow Networks ”, Graves et al 2023
“Likelihood-Based Diffusion Language Models ”, Gulrajani & Hashimoto 2023
“Accelerating Transformer Inference for Translation via Parallel Decoding ”, Santilli et al 2023
Accelerating Transformer Inference for Translation via Parallel Decoding
“Difformer: Empowering Diffusion Model on Embedding Space for Text Generation ”, Gao et al 2022
Difformer: Empowering Diffusion Model on Embedding Space for Text Generation
“Score-Based Continuous-Time Discrete Diffusion Models ”, Sun et al 2022
“CDCD: Continuous Diffusion for Categorical Data ”, Dieleman et al 2022
“Self-Conditioned Embedding Diffusion for Text Generation ”, Strudel et al 2022
“DiffusER: Discrete Diffusion via Edit-Based Reconstruction ”, Reid et al 2022
“DiffuSeq: Sequence to Sequence Text Generation With Diffusion Models ”, Gong et al 2022
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
“Analog Bits: Generating Discrete Data Using Diffusion Models With Self-Conditioning ”, Chen et al 2022
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
“Diffusion-LM Improves Controllable Text Generation ”, Li et al 2022
“Time Control: Language Modeling via Stochastic Processes ”, Wang et al 2022
“Step-Unrolled Denoising Autoencoders for Text Generation ”, Savinov et al 2021
“Zero-Shot Translation Using Diffusion Models ”, Nachmani & Dovrat 2021
“Autoregressive Diffusion Models ”, Hoogeboom et al 2021
“Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models ”, Johnson et al 2021
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models
“Structured Denoising Diffusion Models in Discrete State-Spaces ”, Austin et al 2021
Structured Denoising Diffusion Models in Discrete State-Spaces
“Symbolic Music Generation With Diffusion Models ”, Mittal et al 2021
“Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions ”, Hoogeboom et al 2021
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
generative-models
discrete-diffusion probabilistic-models denoising-data data-ratios conditional-distributions latent-modeling
nlp-diffusion
text-generation-diffusion
Miscellaneous
Bibliography
-
https://arxiv.org/abs/2502.09992: “LLaDA: Large Language Diffusion Models ”, -
https://arxiv.org/abs/2410.18514: “Scaling up Masked Diffusion Models on Text ”, -
https://arxiv.org/abs/2406.04329#deepmind: “Simplified and Generalized Masked Diffusion for Discrete Data ”, -
https://arxiv.org/abs/2311.01017: “Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion ”,