论文概要
这篇综述系统整理图像、视频与 3D/4D 生成中的 reinforcement learning，并把 RL 视为连接不可微目标、偏好反馈、时间结构和高层目标的通用优化接口，而不仅是某种 fine-tuning 算法。
研究问题
Likelihood 和 reconstruction loss 是常用代理目标，但它们可能与视觉质量、语义准确性、物理真实性、可控性和人类偏好不一致。
论文贡献
- 梳理 RL 从经典控制到通用优化与对齐框架的演化。
- 系统组织 RL 在图像、视频和 3D/4D 生成中的集成方式。
- 总结跨领域共同挑战以及 RL 与视觉生成交叉方向的未来问题。
证据与评测范围
作为综述，本文的证据来自对既有文献的分类、归纳和比较，而不是某个新模型的单一 benchmark 提升；应重点参考其 taxonomy、领域章节、表格和开放问题讨论。
适用范围与局限
该方向变化很快，覆盖范围受检索时间和纳入标准约束。后续使用时应以正式版本及其参考文献为入口，检查新工作是否改变已有分类或结论。
Related work 定位
这项工作可作为“RL 在视觉生成中的总体角色”的高层引用，也可作为进入图像、视频和 3D/4D 各子方向文献的导航来源。
Related Work 表述
Liang 等系统综述了视觉生成模型中的 reinforcement learning，覆盖图像、视频和 3D/4D 生成，并将 RL 概括为优化偏好驱动、不可微和结构化目标的通用机制。
这是一段用于说明论文定位的简洁中性表述。
论文官方英文摘要
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.
摘要仅用于学术识别，版权仍归论文作者或出版方所有，不属于本页 CC BY 许可范围。
依据与出处
| 核对内容 | 论文中的位置 |
|---|---|
| 问题陈述 | Abstract |
| 方法与贡献 | Abstract; sections on RL evolution and image, video, and 3D/4D generation |
| 评测结论 | Abstract; domain survey tables and discussion sections |
主要核验来源: Springer journal article (Version of record, published 2026-01-29).
如何引用
科研结论应引用论文本身；只有在复用本站原创解读时才引用本页。
Yuanzhi Liang, Yijie Fang, Rui Li, Ziqi Ni, Ruijie Su, and Chi Zhang. “Integrating reinforcement learning with visual generative models: foundations and advances.” Vicinagearth (2026), 3(1), article 2. https://doi.org/10.1007/s44336-025-00030-z.
复用许可
本页原创解读采用 CC BY 4.0：复用时须署名并链接本页。论文标题、摘要、图表和书目信息不在此许可范围内，仍保留原有权利。 Creative Commons Attribution 4.0 International.