{"total":20,"rows":[{"id":"2501.13918","title":"Improving Video Generation with Human Feedback","created":"2025-01-23","cited_by_count":284,"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":"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":"2503.09151","title":"Reangle-A-Video: 4D Video Generation as Video-to-Video Translation","created":"2025-03-12","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":"2506.03164","title":"Test-Time Scaling of Diffusion Models via Noise Trajectory Search","created":"2025-05-24","cited_by_count":30,"area":"diffusion"},{"id":"2602.05993","title":"Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps","created":"2026-02-05","cited_by_count":29,"area":"diffusion"},{"id":"2509.25170","title":"GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models","created":"2025-09-29","cited_by_count":17,"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":"2411.17041","title":"Free$^2$Guide: Training-Free Text-to-Video Alignment using Image LVLM","created":"2024-11-26","cited_by_count":9,"area":"diffusion"},{"id":"2502.12146","title":"Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory Sharpening","created":"2025-02-17","cited_by_count":7,"area":"diffusion"},{"id":"2507.11554","title":"Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models","created":"2025-07-14","cited_by_count":6,"area":"diffusion"},{"id":"2511.16642","title":"TRIM: Scalable 3D Gaussian Diffusion Inference with Temporal and Spatial Trimming","created":"2025-11-20","cited_by_count":3,"area":"diffusion"},{"id":"2603.01594","title":"Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference","created":"2026-03-02","cited_by_count":2,"area":"diffusion"},{"id":"2605.26491","title":"Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models","created":"2026-05-26","cited_by_count":1,"area":"diffusion"},{"id":"2608.03929","title":"Latent Reward Registers for Diffusion Preference Alignment","created":"2026-08-04","cited_by_count":1,"area":"diffusion"},{"id":"2603.14936","title":"Bridging the Intention-Expression Gap: Aligning Multi-Dimensional Preferences via Hierarchical Relevance Feedback in Text-to-Image Diffusion","created":"2026-03-16","cited_by_count":0,"area":"diffusion"},{"id":"2605.04494","title":"Towards General Preference Alignment: Diffusion Models at Nash Equilibrium","created":"2026-05-06","cited_by_count":0,"area":"diffusion"},{"id":"2605.09065","title":"Dependency-Aware Discrete Diffusion for Scene Graph Generation","created":"2026-05-09","cited_by_count":0,"area":"diffusion"},{"id":"2610.09015","title":"Personalize at Test Time: Learning User Preferences for Image Generation","created":"2026-10-06","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}