Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey

ArXi:2505.17352v2 Announce Type: replace Diffusion models have become a central paradigm for image and multimodal generation, yet their deployment raises persistent questions about alignment, safety, preference satisfaction, and robustness to misuse. This survey reviews recent progress on aligning text-to-image diffusion models through reinforcement learning, reward modeling, preference optimization, and safety-specific fine-tuning.