Training Diffusion Models with Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Black, Kevin, Janner, Michael, Du, Yilun, Kostrikov, Ilya, Levine, Sergey |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Large-scale Reinforcement Learning for Diffusion Models
by: Zhang, Yinan, et al.
Published: (2024)
by: Zhang, Yinan, et al.
Published: (2024)
SPIE: Semantic and Structural Post-Training of Image Editing Diffusion Models with AI feedback
by: Benarous, Elior, et al.
Published: (2025)
by: Benarous, Elior, et al.
Published: (2025)
Vision-Language Models Provide Promptable Representations for Reinforcement Learning
by: Chen, William, et al.
Published: (2024)
by: Chen, William, et al.
Published: (2024)
Equilibrium Matching: Generative Modeling with Implicit Energy-Based Models
by: Wang, Runqian, et al.
Published: (2025)
by: Wang, Runqian, et al.
Published: (2025)
Compositional Generative Modeling: A Single Model is Not All You Need
by: Du, Yilun, et al.
Published: (2024)
by: Du, Yilun, et al.
Published: (2024)
One-step Diffusion Models with $f$-Divergence Distribution Matching
by: Xu, Yilun, et al.
Published: (2025)
by: Xu, Yilun, et al.
Published: (2025)
Grounding Video Models to Actions through Goal Conditioned Exploration
by: Luo, Yunhao, et al.
Published: (2024)
by: Luo, Yunhao, et al.
Published: (2024)
Visual Pre-Training on Unlabeled Images using Reinforcement Learning
by: Ghosh, Dibya, et al.
Published: (2025)
by: Ghosh, Dibya, et al.
Published: (2025)
The Ingredients for Robotic Diffusion Transformers
by: Dasari, Sudeep, et al.
Published: (2024)
by: Dasari, Sudeep, et al.
Published: (2024)
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models
by: Albaba, Mert, et al.
Published: (2025)
by: Albaba, Mert, et al.
Published: (2025)
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
by: Xu, Yilun, et al.
Published: (2024)
by: Xu, Yilun, et al.
Published: (2024)
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
by: Du, Yilun, et al.
Published: (2023)
by: Du, Yilun, et al.
Published: (2023)
AdaWorld: Learning Adaptable World Models with Latent Actions
by: Gao, Shenyuan, et al.
Published: (2025)
by: Gao, Shenyuan, et al.
Published: (2025)
FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling
by: Li, Yitong, et al.
Published: (2026)
by: Li, Yitong, et al.
Published: (2026)
NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training
by: Wu, Fang, et al.
Published: (2026)
by: Wu, Fang, et al.
Published: (2026)
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning
by: Zhai, Yuexiang, et al.
Published: (2024)
by: Zhai, Yuexiang, et al.
Published: (2024)
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
by: Schneider, Moritz, et al.
Published: (2024)
by: Schneider, Moritz, et al.
Published: (2024)
Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments
by: Lillemark, Hansen Jin, et al.
Published: (2026)
by: Lillemark, Hansen Jin, et al.
Published: (2026)
Fast Training of Diffusion Models with Masked Transformers
by: Zheng, Hongkai, et al.
Published: (2023)
by: Zheng, Hongkai, et al.
Published: (2023)
Structure-Guided Adversarial Training of Diffusion Models
by: Yang, Ling, et al.
Published: (2024)
by: Yang, Ling, et al.
Published: (2024)
Diffusion Reinforcement Learning via Centered Reward Distillation
by: Zhu, Yuanzhi, et al.
Published: (2026)
by: Zhu, Yuanzhi, et al.
Published: (2026)
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
by: Yoon, Jaesik, et al.
Published: (2023)
by: Yoon, Jaesik, et al.
Published: (2023)
TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation
by: Monsefi, Amin Karimi, et al.
Published: (2025)
by: Monsefi, Amin Karimi, et al.
Published: (2025)
GARDO: Reinforcing Diffusion Models without Reward Hacking
by: He, Haoran, et al.
Published: (2025)
by: He, Haoran, et al.
Published: (2025)
Autoguided Online Data Curation for Diffusion Model Training
by: Pais, Valeria, et al.
Published: (2025)
by: Pais, Valeria, et al.
Published: (2025)
Personalized Federated Training of Diffusion Models with Privacy Guarantees
by: Patel, Kumar Kshitij, et al.
Published: (2025)
by: Patel, Kumar Kshitij, et al.
Published: (2025)
Pixel-Space Post-Training of Latent Diffusion Models
by: Zhang, Christina, et al.
Published: (2024)
by: Zhang, Christina, et al.
Published: (2024)
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning
by: Kim, Donghu, et al.
Published: (2024)
by: Kim, Donghu, et al.
Published: (2024)
Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data
by: Daras, Giannis, et al.
Published: (2024)
by: Daras, Giannis, et al.
Published: (2024)
One Step Diffusion via Shortcut Models
by: Frans, Kevin, et al.
Published: (2024)
by: Frans, Kevin, et al.
Published: (2024)
Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces
by: Rojas, Kevin, et al.
Published: (2025)
by: Rojas, Kevin, et al.
Published: (2025)
On Memorization in Diffusion Models
by: Gu, Xiangming, et al.
Published: (2023)
by: Gu, Xiangming, et al.
Published: (2023)
On the Difficulty of Learning a Meta-network for Training Data Selection
by: Du, Zilin, et al.
Published: (2026)
by: Du, Zilin, et al.
Published: (2026)
DMin: Scalable Training Data Influence Estimation for Diffusion Models
by: Lin, Huawei, et al.
Published: (2024)
by: Lin, Huawei, et al.
Published: (2024)
DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization
by: Lee, Dongyeun, et al.
Published: (2025)
by: Lee, Dongyeun, et al.
Published: (2025)
NinA: Normalizing Flows in Action. Training VLA Models with Normalizing Flows
by: Tarasov, Denis, et al.
Published: (2025)
by: Tarasov, Denis, et al.
Published: (2025)
Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models
by: Zhang, Gengwei, et al.
Published: (2026)
by: Zhang, Gengwei, et al.
Published: (2026)
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data
by: Aali, Asad, et al.
Published: (2024)
by: Aali, Asad, et al.
Published: (2024)
Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models
by: Wu, Xiaoyu, et al.
Published: (2024)
by: Wu, Xiaoyu, et al.
Published: (2024)
Towards a Mechanistic Explanation of Diffusion Model Generalization
by: Niedoba, Matthew, et al.
Published: (2024)
by: Niedoba, Matthew, et al.
Published: (2024)
Similar Items
-
Large-scale Reinforcement Learning for Diffusion Models
by: Zhang, Yinan, et al.
Published: (2024) -
SPIE: Semantic and Structural Post-Training of Image Editing Diffusion Models with AI feedback
by: Benarous, Elior, et al.
Published: (2025) -
Vision-Language Models Provide Promptable Representations for Reinforcement Learning
by: Chen, William, et al.
Published: (2024) -
Equilibrium Matching: Generative Modeling with Implicit Energy-Based Models
by: Wang, Runqian, et al.
Published: (2025) -
Compositional Generative Modeling: A Single Model is Not All You Need
by: Du, Yilun, et al.
Published: (2024)