Cover Image

Distribution Matching Distillation Meets Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jiang, Dengyang, Liu, Dongyang, Wang, Zanyi, Wu, Qilong, Li, Liuzhuozheng, Li, Hengzhuang, Jin, Xin, Liu, David, Lu, Changsheng, Li, Zhen, Zhang, Bo, Wang, Mengmeng, Hoi, Steven, Gao, Peng, Yang, Harry
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
by: Jiang, Dengyang, et al.
Published: (2026)

Deforming Videos to Masks: Flow Matching for Referring Video Segmentation
by: Wang, Zanyi, et al.
Published: (2025)

Unlocking the Potential of Grounding DINO in Videos: Parameter-Efficient Adaptation for Limited-Data Spatial-Temporal Localization
by: Wang, Zanyi, et al.
Published: (2026)

Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds
by: Li, Liuzhuozheng, et al.
Published: (2026)

Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield
by: Liu, Dongyang, et al.
Published: (2025)

AffordanceSAM: Segment Anything Once More in Affordance Grounding
by: Jiang, Dengyang, et al.
Published: (2025)

RefTon: Reference person shot assist virtual Try-on
by: Li, Liuzhuozheng, et al.
Published: (2025)

Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detection
by: Li, Hengzhuang, et al.
Published: (2025)

SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training
by: Wang, Mengmeng, et al.
Published: (2026)

No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves
by: Jiang, Dengyang, et al.
Published: (2025)

Unleashing the Intrinsic Visual Representation Capability of Multimodal Large Language Models
by: Li, Hengzhuang, et al.
Published: (2025)

Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
by: Image Team, et al.
Published: (2025)

TriCLIP-3D: A Unified Parameter-Efficient Framework for Tri-Modal 3D Visual Grounding based on CLIP
by: Li, Fan, et al.
Published: (2025)

Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning
by: Zhao, Yingnan, et al.
Published: (2025)

LeX-Art: Rethinking Text Generation via Scalable High-Quality Data Synthesis
by: Zhao, Shitian, et al.
Published: (2025)

Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning
by: Li, Chengzu, et al.
Published: (2026)

Probing to Refine: Reinforcement Distillation of LLMs via Explanatory Inversion
by: Tan, Zhen, et al.
Published: (2026)

MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis
by: Liu, Shuhong, et al.
Published: (2024)

Graph Distillation with Eigenbasis Matching
by: Liu, Yang, et al.
Published: (2023)

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
by: Hong, Ziming, et al.
Published: (2025)

DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation
by: Gao, Changsheng, et al.
Published: (2025)

FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution
by: Liu, Shuhong, et al.
Published: (2026)

Continuous-Time Distribution Matching for Few-Step Diffusion Distillation
by: Liu, Tao, et al.
Published: (2026)

Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey
by: Liu, Zhihong, et al.
Published: (2024)

1.x-Distill: Breaking the Diversity, Quality, and Efficiency Barrier in Distribution Matching Distillation
by: Li, Haoyu, et al.
Published: (2026)

Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning
by: Dong, Linwei, et al.
Published: (2026)

Distillation of Discrete Diffusion by Exact Conditional Distribution Matching
by: Gao, Yansong, et al.
Published: (2025)

Robust Dataset Distillation by Matching Adversarial Trajectories
by: Lai, Wei, et al.
Published: (2025)

Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
by: Wu, Tianhe, et al.
Published: (2026)

GeoDM: Geometry-aware Distribution Matching for Dataset Distillation
by: Li, Xuhui, et al.
Published: (2025)

Advances and Perspectives in Organic Semiconductor‐Based Heterojunctions for Photocatalytic Overall Water Splitting
by: Hanwen Liu, et al.
Published: (2026)

Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation
by: Wang, Shaobo, et al.
Published: (2024)

Cross-Resolution Distribution Matching for Diffusion Distillation
by: Chen, Feiyang, et al.
Published: (2026)

DistDD: Distributed Data Distillation Aggregation through Gradient Matching
by: Wang, Peiran, et al.
Published: (2024)

Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
by: Liu, Dingkun, et al.
Published: (2025)

Multimodal Distribution Matching for Vision-Language Dataset Distillation
by: Jeong, Jongoh, et al.
Published: (2026)

Path-Guided Flow Matching for Dataset Distillation
by: Li, Xuhui, et al.
Published: (2026)

Organoids for Cancer Research: Advances and Challenges
by: Miaomaio Xin, et al.
Published: (2024)

Mixture-of-Experts Meets In-Context Reinforcement Learning
by: Wu, Wenhao, et al.
Published: (2025)

Blind Graph Matching Using Graph Signals
by: Liu, Hang, et al.
Published: (2023)