ReALM-GEN: Real-World Constrained and Preference-Aligned Flow- and Diffusion-based Generative Models

Diffusion and flow-based generative models power today’s breakthroughs in Generative AI, showing impressive results in generating various types of data ranging from images and video to protein molecules and text. However, making them respect real-world constraints and align with users' preferences at post-training phase or at inference time, is still an unsolved challenge. ReALM-GEN at ICLR 2026 will bring together a diverse community of researchers spanning theoretical foundations of ML and generative models, vision, language, robotics, and scientific applications of AI, to explore bold ideas and practical tools for adapting and/or steering pretrained flow- and diffusion-based models toward real-world constraint satisfaction and alignment with user preferences.