[2608.14430] Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14430 (cs) [Submitted on 14 Aug 2026] Title: Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View Authors: Yixian Xu , Yuanrui Zhang , Shengjie Luo , Liwei Wang , Di He View a PDF of the paper titled Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View, by Yixian Xu and 4 other authors View PDF HTML (experimental) Abstract: 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. Comments: 29 pages, 9 figures, 4 tables; work in progress Subjects: Machine Learning (cs.LG) ; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML) Cite as: arXiv:2608.14430 [cs.LG] (or arXiv:2608.14430v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14430 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuanrui Zhang [ view email ] [v1] Fri, 14 Aug 2026 16:11:55 UTC (6,494 KB) Full-text links: Access Paper: View a PDF of the paper titled Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View, by Yixian Xu and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CV stat stat.ML References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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