The field of visual content generation is witnessing a significant shift towards the integration of reinforcement learning (RL) techniques. This movement is driven by the need for more controllable, consistent, and human-aligned generation of visual content, including images, videos, and 3D/4D structures. RL offers a principled framework for optimizing non-differentiable, preference-driven, and temporally structured objectives, which is essential for achieving high-quality and realistic visual content. The use of RL in visual content generation has been shown to improve the quality and diversity of generated content, and has the potential to enable more effective and efficient generation of visual content. Notable papers in this area include: AR-GRPO, which proposes a novel approach to integrate online RL training into autoregressive image generation models, and Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representation, which introduces a shared embedding space trained via quadruple contrastive learning across modalities and human-AI styles.
Reinforcement Learning in Visual Content Generation
Sources
AR-GRPO: Training Autoregressive Image Generation Models via Reinforcement Learning
Reinforcement Learning in Vision: A Survey
Multi-Objective Instruction-Aware Representation Learning in Procedural Content Generation RL
ADT4Coupons: An Innovative Framework for Sequential Coupon Distribution in E-commerce
Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization
Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representation
Integrating Reinforcement Learning with Visual Generative Models: Foundations and Advances