arXiv AI
Jul 2
PAPA: Online Personalized Active Preference Alignment
arXiv:2607. 00486v1 Announce Type: cross Abstract: Diffusion models are highly effective at modeling complex data distributions, including images and text.
Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models
The paper introduces Diffusion LAIR, a listwise preference optimization technique that leverages continuous reward scores instead of binary pairwise comparisons to align text‑to‑image diffusion models. LAIR transforms reward scores into centered advantage weights and optimizes an advantage‑weighted regression objective on an implicit reward defined by denoising‑loss improvement over a reference model, with a quadratic penalty to regulate reward magnitude. Experiments demonstrate that Diffusion LAIR surpasses strong baseline methods on SD1.5 and SDXL across generation, compositional, and editing tasks.
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STAR: SpatioTemporal Adaptive Reward Allocation for Text-to-Image RL Post-Training
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arXiv Computer Vision
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Hugging Face Trending Papers
Jun 1
Drifting Preference Optimization for One-Step Generative Models
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arXiv AI
Jul 7
MARS: Margin and Semantic-Aware Data Augmentation for Reward Modeling
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arXiv AI
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Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling
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arXiv Machine Learning
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Latent Reward Registers for Diffusion Preference Alignment
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arXiv Machine Learning
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Drifting Preference Optimization for One-Step Generative Models
arXiv:2606. 02521v1 Announce Type: new Abstract: One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning them remains difficult: standard alignment methods often rely on policy likelihoods, denoising trajectories, differentiable reward gradients, or test-time optimization.
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arXiv Machine Learning
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Scaling Reinforcement Learning for Diffusion Models via Velocity Matching
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