Recent advances in few-step diffusion distillation have enabled efficient image generation, yet aligning these models with human preferences remains challenging. We propose Reward-Tilted Distribution Matching Distillation (RTDMD), a two-stage framework that unifies distribution matching distillation with reward-guided reinforcement learning for few-step flow generators. We show that minimizing the KL divergence to a reward-tilted teacher distribution naturally decomposes into a distribution matching term and a reward maximization term. In the first stage, we introduce Ambient-Consistent Distribution Matching Distillation (AC-DMD), which performs subinterval-wise distribution matching and augments the fake score objective with a consistency regularizer to help the fake score model track the shifting generator distribution under limited updates. In the second stage, we jointly optimize both terms: for the reward maximization term, we derive a hybrid policy gradient that combines a GRPO-style estimator for the stochastic intermediate transitions with direct reward backpropagation through the deterministic final step, and further introduce step-subset GRPO (SubGRPO) to reduce variance. Experiments on SD3, SD3.5, and FLUX.2 demonstrate that RTDMD establishes new state-of-the-art results across preference, aesthetic, and compositional metrics with only 4 inference steps, outperforming previous few-step text-to-image generation methods. Code and models are available at https://github.com/Harahan/RTDMD.

翻译：近期在少步扩散蒸馏方面取得的进展已实现高效图像生成，然而使这些模型与人类偏好对齐仍面临挑战。我们提出奖励倾斜分布匹配蒸馏（RTDMD）——一种将分布匹配蒸馏与奖励引导强化学习统一的两阶段框架，专为少步流生成器设计。研究表明，最小化与奖励倾斜教师分布之间的KL散度可自然分解为分布匹配项和奖励最大化项。在第一阶段，我们引入环境一致性分布匹配蒸馏（AC-DMD），该方法执行子区间级分布匹配，并通过一致性正则化增强虚假评分目标，以帮助虚假评分模型在有限更新次数下追踪动态变化的生成器分布。第二阶段中，我们对两项指标进行联合优化：针对奖励最大化项，我们推导出混合策略梯度，该梯度将GRPO风格的随机中间状态转移估计器与通过确定性最终步的直接奖励反向传播相结合，并进一步引入子集GRPO（SubGRPO）以降低方差。在SD3、SD3.5和FLUX.2上的实验表明，RTDMD仅需4次推理步即在偏好、美学和组合指标上创下新的最优结果，性能超越以往所有少步文本生成图像方法。代码与模型已开源至 https://github.com/Harahan/RTDMD。