这篇论文用多轴Max@K强化学习，让T2I模型生成更公平多样的人像，公平分提升0.23-0.36，不牺牲质量。
文本到图像模型如SD3.5-M对同一提示常生成有限视觉模式的图像，加剧人口统计偏斜。论文形式化目标为target-mode覆盖，提出多轴Max@K强化学习目标。该方法对每组样本取每个类别的最大分数并求和，分配正权重仅当样本提升该类别的组最大值。在SD3.5-M上使用确定性颜色奖励验证信用分配机制后，在三个自动评估器上公平性分数相对于基础模型提升0.23-0.36，同时保持图像质量和文本对齐。
Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation
Text-to-image (T2I) models can synthesize realistic, prompt-aligned images, yet samples generated for the same prompt often cover only a small subset of visually distinct modes. This limits the diversity of images, and for person-centric prompts, can reflect or amplify demographic skew. We formalize this problem as coverage of a predefined set of semantically specified modes, which we call target-mode coverage. We then propose multi-axis max@K, a group-based reinforcement learning objective for improving such coverage in diffusion-based T2I models. Given a group of samples and one score per target category, multi-axis max@K first takes the maximum score across samples for each category and then sums these category-wise maxima. The resulting credit assignment gives a sample positive weight on a category only when it increases that category's group-wise maximum, allowing different samples to contribute to different categories. We first validate the credit-assignment mechanism on a synthetic mixture and on SD3.5-M using deterministic pixel-based color rewards. We then evaluate the same objective on perceived-appearance fairness. Across three automatic evaluators on held-out prompts, multi-axis max@K improves the Fairness Score by 0.23-0.36 relative to the base model, while maintaining image quality and text alignment.