{"total":18,"rows":[{"id":"2406.04312","title":"ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization","created":"2024-06-06","cited_by_count":119,"area":"alignment"},{"id":"2503.11701","title":"A Survey of Direct Preference Optimization","created":"2025-03-12","cited_by_count":36,"area":"alignment"},{"id":"2410.18013","title":"Scalable Ranked Preference Optimization for Text-to-Image Generation","created":"2024-10-23","cited_by_count":33,"area":"alignment"},{"id":"2409.09774","title":"Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through $f$-divergence Minimization","created":"2024-09-15","cited_by_count":15,"area":"alignment"},{"id":"2501.02790","title":"Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model","created":"2025-01-06","cited_by_count":12,"area":"alignment"},{"id":"2506.18655","title":"RDPO: Real Data Preference Optimization for Physics Consistency Video Generation","created":"2025-06-23","cited_by_count":9,"area":"alignment"},{"id":"2602.02033","title":"One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generation","created":"2026-02-02","cited_by_count":5,"area":"alignment"},{"id":"2604.24952","title":"Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization","created":"2026-04-27","cited_by_count":4,"area":"alignment"},{"id":"2602.19180","title":"VLM-Guided Group Preference Alignment for Diffusion-based Human Mesh Recovery","created":"2026-02-22","cited_by_count":2,"area":"alignment"},{"id":"2604.24953","title":"ViPO: Visual Preference Optimization at Scale","created":"2026-04-27","cited_by_count":2,"area":"alignment"},{"id":"2605.04653","title":"Threshold-Guided Optimization for Visual Generative Models","created":"2026-05-06","cited_by_count":2,"area":"alignment"},{"id":"2510.25897","title":"MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency","created":"2025-10-29","cited_by_count":1,"area":"alignment"},{"id":"2511.04753","title":"CPO: Condition Preference Optimization for Controllable Image Generation","created":"2025-11-06","cited_by_count":1,"area":"alignment"},{"id":"2604.08723","title":"Decomposing the Delta: What Do Models Actually Learn from Preference Pairs?","created":"2026-04-09","cited_by_count":1,"area":"alignment"},{"id":"2607.29025","title":"Evaluation-Verification Reward for Consistent Multi-Reference Image Editing","created":"2026-07-31","cited_by_count":1,"area":"alignment"},{"id":"2602.19423","title":"Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy","created":"2026-02-23","cited_by_count":0,"area":"alignment"},{"id":"2605.07545","title":"Implicit Preference Alignment for Human Image Animation","created":"2026-05-08","cited_by_count":0,"area":"alignment"},{"id":"2605.25876","title":"DyCoRM: Dynamic Criterion-Aware Reward Modeling for Text-to-Image Generation","created":"2026-05-25","cited_by_count":0,"area":"alignment"}],"next_cursor":null}