{"total":208,"rows":[{"id":"2305.13301","title":"Training Diffusion Models with Reinforcement Learning","created":"2023-05-22","cited_by_count":982,"area":"diffusion"},{"id":"2305.16381","title":"DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models","created":"2023-05-25","cited_by_count":450,"area":"diffusion"},{"id":"2309.17400","title":"Directly Fine-Tuning Diffusion Models on Differentiable Rewards","created":"2023-09-29","cited_by_count":448,"area":"diffusion"},{"id":"2310.03739","title":"Aligning Text-to-Image Diffusion Models with Reward Backpropagation","created":"2023-10-05","cited_by_count":288,"area":"diffusion"},{"id":"2303.14420","title":"Human Preference Score: Better Aligning Text-to-Image Models with Human Preference","created":"2023-03-25","cited_by_count":287,"area":"diffusion"},{"id":"2501.13918","title":"Improving Video Generation with Human Feedback","created":"2025-01-23","cited_by_count":284,"area":"diffusion"},{"id":"2305.18292","title":"Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models","created":"2023-05-29","cited_by_count":261,"area":"diffusion"},{"id":"2311.13231","title":"Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model","created":"2023-11-22","cited_by_count":252,"area":"diffusion"},{"id":"2509.16117","title":"DiffusionNFT: Online Diffusion Reinforcement with Forward Process","created":"2025-09-19","cited_by_count":230,"area":"diffusion"},{"id":"2507.21802","title":"MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE","created":"2025-07-29","cited_by_count":178,"area":"diffusion"},{"id":"2306.05427","title":"Grounded Text-to-Image Synthesis with Attention Refocusing","created":"2023-06-08","cited_by_count":164,"area":"diffusion"},{"id":"2402.15194","title":"Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control","created":"2024-02-23","cited_by_count":143,"area":"diffusion"},{"id":"2407.08737","title":"Video Diffusion Alignment via Reward Gradients","created":"2024-07-11","cited_by_count":107,"area":"diffusion"},{"id":"2404.04465","title":"Aligning Diffusion Models by Optimizing Human Utility","created":"2024-04-06","cited_by_count":101,"area":"diffusion"},{"id":"2507.21053","title":"Flow Matching Policy Gradients","created":"2025-07-28","cited_by_count":99,"area":"diffusion"},{"id":"2407.13734","title":"Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review","created":"2024-07-18","cited_by_count":98,"area":"diffusion"},{"id":"2501.05803","title":"Test-time Alignment of Diffusion Models without Reward Over-optimization","created":"2025-01-10","cited_by_count":93,"area":"diffusion"},{"id":"2312.12490","title":"InstructVideo: Instructing Video Diffusion Models with Human Feedback","created":"2023-12-19","cited_by_count":86,"area":"diffusion"},{"id":"2402.10210","title":"Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation","created":"2024-02-15","cited_by_count":85,"area":"diffusion"},{"id":"2406.04314","title":"Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization","created":"2024-06-06","cited_by_count":84,"area":"diffusion"},{"id":"2401.12244","title":"Large-scale Reinforcement Learning for Diffusion Models","created":"2024-01-20","cited_by_count":78,"area":"diffusion"},{"id":"2404.07771","title":"An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization","created":"2024-04-11","cited_by_count":73,"area":"diffusion"},{"id":"2509.25050","title":"Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion Models","created":"2025-09-29","cited_by_count":67,"area":"diffusion"},{"id":"2402.07487","title":"Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial","created":"2024-02-12","cited_by_count":60,"area":"diffusion"},{"id":"2405.00760","title":"Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models","created":"2024-05-01","cited_by_count":59,"area":"diffusion"},{"id":"2402.08265","title":"A Dense Reward View on Aligning Text-to-Image Diffusion with Preference","created":"2024-02-13","cited_by_count":55,"area":"diffusion"},{"id":"2402.16359","title":"Feedback Efficient Online Fine-Tuning of Diffusion Models","created":"2024-02-26","cited_by_count":53,"area":"diffusion"},{"id":"2406.10210","title":"Make It Count: Text-to-Image Generation with an Accurate Number of Objects","created":"2024-06-14","cited_by_count":47,"area":"diffusion"},{"id":"2409.09347","title":"Schr\\\"odinger Bridge Flow for Unpaired Data Translation","created":"2024-09-14","cited_by_count":47,"area":"diffusion"},{"id":"2406.06424","title":"Margin-aware Preference Optimization for Aligning Diffusion Models without Reference","created":"2024-06-10","cited_by_count":46,"area":"diffusion"},{"id":"2502.06061","title":"Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein Regularization","created":"2025-02-09","cited_by_count":43,"area":"diffusion"},{"id":"2402.08714","title":"PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models","created":"2024-02-13","cited_by_count":42,"area":"diffusion"},{"id":"2410.11795","title":"Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices","created":"2024-10-15","cited_by_count":41,"area":"diffusion"},{"id":"2412.15159","title":"OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization","created":"2024-12-19","cited_by_count":41,"area":"diffusion"},{"id":"2501.19252","title":"Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search","created":"2025-01-31","cited_by_count":41,"area":"diffusion"},{"id":"2502.02588","title":"Calibrated Multi-Preference Optimization for Aligning Diffusion Models","created":"2025-02-04","cited_by_count":41,"area":"diffusion"},{"id":"2305.13308","title":"If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection","created":"2023-05-22","cited_by_count":40,"area":"diffusion"},{"id":"2312.03626","title":"TokenCompose: Text-to-Image Diffusion with Token-level Supervision","created":"2023-12-06","cited_by_count":40,"area":"diffusion"},{"id":"2405.13637","title":"Curriculum Direct Preference Optimization for Diffusion and Consistency Models","created":"2024-05-22","cited_by_count":36,"area":"diffusion"},{"id":"2403.11027","title":"Reward Guided Latent Consistency Distillation","created":"2024-03-16","cited_by_count":35,"area":"diffusion"},{"id":"2409.07253","title":"Alignment of Diffusion Models: Fundamentals, Challenges, and Future","created":"2024-09-11","cited_by_count":35,"area":"diffusion"},{"id":"2506.03517","title":"DenseDPO: Fine-Grained Temporal Preference Optimization for Video Diffusion Models","created":"2025-06-04","cited_by_count":34,"area":"diffusion"},{"id":"2506.20701","title":"Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models","created":"2025-06-25","cited_by_count":34,"area":"diffusion"},{"id":"2404.04243","title":"Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models","created":"2024-04-05","cited_by_count":33,"area":"diffusion"},{"id":"2410.07171","title":"IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation","created":"2024-10-09","cited_by_count":33,"area":"diffusion"},{"id":"2502.01819","title":"Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning","created":"2025-02-03","cited_by_count":33,"area":"diffusion"},{"id":"2307.04749","title":"Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback","created":"2023-07-10","cited_by_count":32,"area":"diffusion"},{"id":"2406.06382","title":"Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization","created":"2024-06-10","cited_by_count":32,"area":"diffusion"},{"id":"2412.07775","title":"Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets","created":"2024-12-10","cited_by_count":32,"area":"diffusion"},{"id":"2503.11240","title":"Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards","created":"2025-03-14","cited_by_count":32,"area":"diffusion"},{"id":"2410.18881","title":"Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences","created":"2024-10-24","cited_by_count":31,"area":"diffusion"},{"id":"2311.15657","title":"Enhancing Diffusion Models with Text-Encoder Reinforcement Learning","created":"2023-11-27","cited_by_count":30,"area":"diffusion"},{"id":"2402.08552","title":"Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases","created":"2024-02-13","cited_by_count":30,"area":"diffusion"},{"id":"2503.00897","title":"A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning","created":"2025-03-02","cited_by_count":30,"area":"diffusion"},{"id":"2509.06942","title":"Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference","created":"2025-09-08","cited_by_count":30,"area":"diffusion"},{"id":"2305.10843","title":"X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models","created":"2023-05-18","cited_by_count":28,"area":"diffusion"},{"id":"2401.10061","title":"DiffusionAgent: Navigating Expert Models for Agentic Image Generation","created":"2024-01-18","cited_by_count":28,"area":"diffusion"},{"id":"2403.18978","title":"TextCraftor: Your Text Encoder Can be Image Quality Controller","created":"2024-03-27","cited_by_count":27,"area":"diffusion"},{"id":"2406.00633","title":"Improving GFlowNets for Text-to-Image Diffusion Alignment","created":"2024-06-02","cited_by_count":27,"area":"diffusion"},{"id":"2412.00759","title":"DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling","created":"2024-12-01","cited_by_count":27,"area":"diffusion"},{"id":"2308.13142","title":"A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions","created":"2023-08-25","cited_by_count":25,"area":"diffusion"},{"id":"2405.19673","title":"Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models","created":"2024-05-30","cited_by_count":25,"area":"diffusion"},{"id":"2306.17567","title":"Counting Guidance for High Fidelity Text-to-Image Synthesis","created":"2023-06-30","cited_by_count":24,"area":"diffusion"},{"id":"2503.15927","title":"BlockDance: Reuse Structurally Similar Spatio-Temporal Features to Accelerate Diffusion Transformers","created":"2025-03-20","cited_by_count":24,"area":"diffusion"},{"id":"2505.02648","title":"MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation","created":"2025-05-05","cited_by_count":23,"area":"diffusion"},{"id":"2312.13980","title":"Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning","created":"2023-12-21","cited_by_count":22,"area":"diffusion"},{"id":"2501.06655","title":"Personalized Preference Fine-tuning of Diffusion Models","created":"2025-01-11","cited_by_count":22,"area":"diffusion"},{"id":"2311.14768","title":"AdaDiff: Adaptive Step Selection for Fast Diffusion Models","created":"2023-11-24","cited_by_count":20,"area":"diffusion"},{"id":"2408.15094","title":"Constrained Diffusion Models via Dual Training","created":"2024-08-27","cited_by_count":20,"area":"diffusion"},{"id":"2311.13833","title":"Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models","created":"2023-11-23","cited_by_count":19,"area":"diffusion"},{"id":"2409.04832","title":"Reward-Directed Score-Based Diffusion Models via q-Learning","created":"2024-09-07","cited_by_count":19,"area":"diffusion"},{"id":"2409.08400","title":"Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning","created":"2024-09-12","cited_by_count":19,"area":"diffusion"},{"id":"2412.18164","title":"Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence","created":"2024-12-24","cited_by_count":19,"area":"diffusion"},{"id":"2503.13070","title":"Reward-Instruct: A Reward-Centric Approach to Fast Photo-Realistic Image Generation","created":"2025-03-17","cited_by_count":19,"area":"diffusion"},{"id":"2512.24146","title":"Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning","created":"2025-12-30","cited_by_count":18,"area":"diffusion"},{"id":"2406.12120","title":"Adding Conditional Control to Diffusion Models with Reinforcement Learning","created":"2024-06-17","cited_by_count":16,"area":"diffusion"},{"id":"2411.15247","title":"Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward","created":"2024-11-22","cited_by_count":16,"area":"diffusion"},{"id":"2306.12438","title":"Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback","created":"2023-06-16","cited_by_count":15,"area":"diffusion"},{"id":"2312.06038","title":"Correcting Diffusion Generation through Resampling","created":"2023-12-10","cited_by_count":15,"area":"diffusion"},{"id":"2501.08259","title":"FDPP: Fine-tune Diffusion Policy with Human Preference","created":"2025-01-14","cited_by_count":15,"area":"diffusion"},{"id":"2506.01320","title":"Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models","created":"2025-06-02","cited_by_count":15,"area":"diffusion"},{"id":"2505.22002","title":"D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples","created":"2025-05-28","cited_by_count":14,"area":"diffusion"},{"id":"2512.05116","title":"Value Gradient Guidance for Flow Matching Alignment","created":"2025-12-04","cited_by_count":14,"area":"diffusion"},{"id":"2402.16302","title":"Graph Diffusion Policy Optimization","created":"2024-02-26","cited_by_count":13,"area":"diffusion"},{"id":"2506.02698","title":"Smoothed Preference Optimization via ReNoise Inversion for Aligning Diffusion Models with Varied Human Preferences","created":"2025-06-03","cited_by_count":13,"area":"diffusion"},{"id":"2603.23500","title":"UniGRPO: Unified Policy Optimization for Reasoning-Driven Visual Generation","created":"2026-03-24","cited_by_count":13,"area":"diffusion"},{"id":"2307.02770","title":"Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback","created":"2023-07-06","cited_by_count":12,"area":"diffusion"},{"id":"2411.11727","title":"Aligning Few-Step Diffusion Models with Dense Reward Difference Learning","created":"2024-11-18","cited_by_count":12,"area":"diffusion"},{"id":"2511.01678","title":"UniLumos: Fast and Unified Image and Video Relighting with Physics-Plausible Feedback","created":"2025-11-03","cited_by_count":12,"area":"diffusion"},{"id":"2407.06642","title":"Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning","created":"2024-07-09","cited_by_count":11,"area":"diffusion"},{"id":"2602.05533","title":"Conditional Diffusion Guidance under Hard Constraint: A Stochastic Analysis Approach","created":"2026-02-05","cited_by_count":11,"area":"diffusion"},{"id":"2602.22801","title":"Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving","created":"2026-02-26","cited_by_count":11,"area":"diffusion"},{"id":"2608.00146","title":"DiffusionGemma Technical Report","created":"2026-07-31","cited_by_count":11,"area":"diffusion"},{"id":"2404.04860","title":"ByteEdit: Boost, Comply and Accelerate Generative Image Editing","created":"2024-04-07","cited_by_count":10,"area":"diffusion"},{"id":"2405.16876","title":"Transfer Learning for Diffusion Models","created":"2024-05-27","cited_by_count":10,"area":"diffusion"},{"id":"2407.21032","title":"Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion","created":"2024-07-17","cited_by_count":10,"area":"diffusion"},{"id":"2502.01690","title":"HuViDPO:Enhancing Video Generation through Direct Preference Optimization for Human-Centric Alignment","created":"2025-02-02","cited_by_count":10,"area":"diffusion"},{"id":"2508.19104","title":"Composition and Alignment of Diffusion Models using Constrained Learning","created":"2025-08-26","cited_by_count":10,"area":"diffusion"},{"id":"2508.21016","title":"Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance","created":"2025-08-28","cited_by_count":10,"area":"diffusion"},{"id":"2510.01549","title":"MIRA: Towards Mitigating Reward Hacking in Inference-Time Alignment of T2I Diffusion Models","created":"2025-10-02","cited_by_count":9,"area":"diffusion"}],"next_cursor":"100"}