{
  "id": "liang2026rlvisualgeneration",
  "type": "article-journal",
  "title": "Integrating reinforcement learning with visual generative models: foundations and advances",
  "author": [
    {
      "given": "Yuanzhi",
      "family": "Liang"
    },
    {
      "given": "Yijie",
      "family": "Fang"
    },
    {
      "given": "Rui",
      "family": "Li"
    },
    {
      "given": "Ziqi",
      "family": "Ni"
    },
    {
      "given": "Ruijie",
      "family": "Su"
    },
    {
      "given": "Chi",
      "family": "Zhang"
    }
  ],
  "container-title": "Vicinagearth",
  "issued": {
    "date-parts": [
      [
        2026,
        1,
        29
      ]
    ]
  },
  "URL": "https://doi.org/10.1007/s44336-025-00030-z",
  "abstract": "Generative models have made significant progress in synthesizing visual content, including images, videos, and 3D/4D structures. However, they are typically trained with surrogate objectives such as likelihood or reconstruction loss, which often misalign with perceptual quality, semantic accuracy, or physical realism. Reinforcement learning (RL) offers a principled framework for optimizing non-differentiable, preference-driven, and temporally structured objectives. Recent advances demonstrate its effectiveness in enhancing controllability, consistency, and human alignment across generative tasks. This survey provides a systematic overview of RL-based methods for visual content generation. We review the evolution of RL from classical control to its role as a general-purpose optimization tool, and examine its integration into image, video, and 3D/4D generation. Across these domains, RL serves not only as a fine-tuning mechanism but also as a structural component for aligning generation with complex, high-level goals. We conclude with open challenges and future research directions at the intersection of RL and generative modeling.",
  "keyword": "reinforcement learning, visual generative models, image generation, video generation, 3D and 4D generation, survey",
  "publisher": "Springer Nature",
  "DOI": "10.1007/s44336-025-00030-z",
  "volume": "3",
  "issue": "1",
  "page": "2"
}