The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2606. 19162v1 Announce Type: new Abstract: Score- and flow-matching models often rely on preference-based reinforcement learning for two purposes: aligning with subjective preferences and, surprisingly, recovering properties such as visual realism and coherent object structure that matching-based training is intended to learn from the data itself.
The paper introduces SatisDive, a training‑free inference method that balances reward and diversity in text‑to‑image diffusion by enforcing a reward floor for each image and a diversity cutoff for the batch. By adjusting the reward floor, the method traces a Pareto frontier between worst‑candidate reward and batch diversity. Experiments on Pick‑a‑Pic show that SatisDive consistently outperforms FK steering, improving worst‑candidate reward by up to 0.70 in some settings and Pareto‑dominating FK steering across overlapping DreamSim ranges.
arXiv:2606. 17979v1 Announce Type: new Abstract: Existing RL post-training methods for text-to-image generation usually convert the final-image reward into a single scalar advantage and apply it with the same strength to the entire generative trajectory.
arXiv:2608.21425v1 Announce Type: cross Abstract: Video generation is central to AI-powered content creation. Aligning generated videos with human preferences is a key criterion for evaluating genera...
arXiv:2608. 09385v1 Announce Type: cross Abstract: Generative AI models are primarily designed to imitate the data distribution, an objective that neither corrects diversity lost by a learned generator nor defines how generation should extend beyond the diversity of the data itself.
arXiv:2601. 12401v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human preferences and user-specified tasks.