这篇论文把Flow-GRPO、AWM、DiffusionNFT几套方法统一到一个框架里，说清了它们差别只是方差缩减，还在SD3.5-M和Qwen-Image上跑赢了之前的方案。
现有扩散模型强化学习算法分为反向轨迹与前向匹配两类，分别依赖离散化似然比和带奖励标签的噪声版本。本文从正则化扩散RL目标出发，用重要性采样得到轨迹空间的策略梯度估计，其中含Flow-GRPO式Itô积分。作者推导出等价的方差缩减值梯度形式，恢复AWM和DiffusionNFT的前向匹配结构，把两类方法的差异归因于方差缩减而非RL原则。在SD3.5-M和Qwen-Image模型上的实验验证了这一解释，并取得优于既有扩散RL基线的结果。
Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View
Reinforcement learning (RL) post-training provides a direct way to align diffusion models with human preferences and task-specific rewards. However, current RL algorithms for diffusion models remain fragmented: reverse-trajectory methods rely on discretized likelihood ratios, whereas forward-matching methods train on reward-labeled noising versions of the rollout samples. This paper shows that these seemingly different losses arise from a single path-space principle. Starting from the regularized diffusion-RL objective, we use importance sampling between sampling SDEs to obtain an explicit policy-gradient estimator on trajectory space. The estimator contains the stochastic Itô integral underlying Flow-GRPO-type updates; we derive an equivalent variance-reduced value-gradient form that recovers the forward-matching structure of AWM and DiffusionNFT. This identifies the empirical gap between these method families as a variance-reduction effect rather than a difference in RL principle. The derivation yields a unified design space organized by value-gradient estimation, weight functions, and sampling choices. Within this space, we propose a multi-sample KDE value-gradient estimator that reuses rollout groups, together with scale-bounded weight families that retain stable existing recipes while excluding singular ones. Experiments on SD3.5-M and Qwen-Image models validate the variance-reduction explanation and show that the resulting recipe improves over prior diffusion-RL baselines.