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Diffusion models have achieved state-of-the-art performance across multiple domains, with recent advancements extending their applicability to discrete data.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Namrata Anand and Tudor Achim · 2022
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A continuous time framework for discrete denoising models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, and Arnaud Doucet · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Training diffusion models with reinforcement learning
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
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Discrete diffusion language modeling by estimating the ratios of the data distribution
Aaron Lou, Chenlin Meng, and Stefano Ermon · 2023
Earlier work this paper cites.
De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
Cited alongside, same era.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
Cited alongside, same era.
A general theoretical paradigm to understand learning from human preferences
Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, and Daniele Calandriello · 2024
Cited alongside, same era.
Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola · 2024
Cited alongside, same era.
Kto: Model alignment as prospect theoretic optimization
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela · 2024
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Simple and effective masked diffusion language models
Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, and Volodymyr Kuleshov · 2024
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Designing dna with tunable regulatory activity using discrete diffusion
Anirban Sarkar, Ziqi Tang, Chris Zhao, and Peter Koo · 2024
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Simplified and generalized masked diffusion for discrete data
Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis K Titsias · 2024
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Diffusion model alignment using direct preference optimization
Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik · 2024
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Cited alongside, same era.
Reinforcement learning for fine-tuning text-to-image diffusion models
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, and Kimin Lee · 2024
Cited alongside, same era.
Aligning diffusion models by optimizing human utility
Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Yusuke Kato, and Kazuki Kozuka · 2024
Cited alongside, same era.
Unlocking guidance for discrete state-space diffusion and flow models
Hunter Nisonoff, Junhao Xiong, Stephan Allenspach, and Jennifer Listgarten · 2024
Cited alongside, same era.
Your absorbing discrete diffusion secretly models the conditional distributions of clean data
Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, and Chongxuan Li · 2024
Cited alongside, same era.
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Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi Jaakkola, Sergey Levine, Hanchen Wang, and Aviv Regev · 2024
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Aligning protein generative models with experimental fitness via direct preference optimization
Talal Widatalla, Rafael Rafailov, and Brian Hie · 2024
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Using human feedback to fine-tune diffusion models without any reward model
Kai Yang, Jian Tao, Jiafei Lyu, Chunjiang Ge, Jiaxin Chen, Weihan Shen, Xiaolong Zhu, and Xiu Li · 2024
Later among the works it cites.
DSPO: Direct score preference optimization for diffusion model alignment
Huaisheng Zhu, Teng Xiao, and Vasant G Honavar · 2025
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