The paper introduces a post‑training framework for visual generative models that enhances specific attributes while preserving overall quality. It combines Generator‑Aligned Reward Modeling (GARM) to align reward signals with the generator’s distribution and reduce content confounding, with Target‑Aware Bounded Optimization (TABO) that caps reward values using real target examples. The authors also present TA‑GRPO, a flow‑based video optimization variant that further improves attribute enhancement and mitigates unintended attribute leakage.
By Ziqiao Weng, Yuanzhi Liang, Haibing Huang, Weidong Cai, Xuelong Li
The paper introduces Objective-aware Trajectory Credit Assignment (OTCA), a framework that refines reinforcement learning for diffusion-based visual generation. OTCA decomposes credit across denoising steps and allocates multiple reward signals adaptively, addressing the coarse, uniform reward assignment of existing GRPO pipelines. Experiments demonstrate that OTCA consistently enhances image and video generation quality across various metrics.
By Rui Li, Ke Hao, Yuanzhi Liang, Haibin Huang, Chi Zhang, Yun Gu, Xuelong Li
arXiv:2608.30194v1 Announce Type: new
Abstract: Diffusion models have recently advanced text-to-video (T2V) generation, yet they still struggle with fine-grained compositional alignment, such as attr...
By Yujiang Pu, Yu Kong
arXiv:2610.01973v1 Announce Type: new
Abstract: Reinforcement learning (RL) for video generation usually assigns one scalar reward to an entire sampled video. Yet a video is not uniformly flawed: som...
By Yifan Wang, Gordon Guocheng Qian, Yanyu Li, Anil Kag, Yun Fu
arXiv:2610.03423v1 Announce Type: new
Abstract: Reinforcement learning (RL) for Text-to-3D (T23D) generation requires optimization across multiple quality dimensions such as semantic alignment and te...
By Bingyang Cui, Yujie Zhang, Yiling Xu, Yunfeng Guan
arXiv:2610.16632v1 Announce Type: cross
Abstract: Memory-based long-video generation aims to combine user control with long-range visual consistency. We organize this process into three functional co...
By Hang Xu, Guohui Zhang, Xiaoxiao Ma, Hu Yu, Jie Huang, Jiayin Xiao, Lin Song, Haoyang Huang, Nan Duan, Feng Zhao
AV‑GRPO introduces a modality‑anchored diffusion reinforcement learning framework for joint audio‑video generation, addressing limitations in fidelity, text‑modality alignment, and cross‑modal synchronization. It decouples learning signals through modality‑anchored rollouts, employs trajectory‑locked frozen‑tower optimization to reduce computational cost, and adapts objectives to each modality’s dynamics. The accompanying 5DAV dataset provides difficulty‑controllable, decoupled training samples, and experiments on JavisBench and VABench show AV‑GRPO surpasses LTX‑2.3 in generation quality, semantic alignment, and synchronization.
By Zhiyu Xu, Weilong Yan, Yufei Shi, Shiyang Li, Yihao Liu, Kin-Man Lam, Yuewen Cao
The paper introduces Effect-Marginal Policy Optimization (EMPO), a method that addresses credit-update misalignment in multi-turn reinforcement learning for large language model agents. EMPO aligns policy updates with execution-level equivalence by using an effect-level surrogate that couples updates across different textual responses that produce the same effect. Experiments on ALFWorld, WebShop, and Sokoban demonstrate that EMPO improves both task performance and learning efficiency compared to strong baselines.
By Shaojin Ma, Ruitao Wang, Pengyi Li, Jie Yang, Liangyu Chen, Gangyi Zhao, Hongyao Tang, Yan Zheng, Jianye Hao
The paper investigates how fine‑grained credit assignment affects learning in multimodal agentic reinforcement learning, using video editing as a testbed. Four movie‑trailer editing tasks—Content Selection, Narrative Sequencing, Rough Cut, and Fine Cut—are introduced to represent real‑world editing workflows with varying complexity and horizon lengths. The study finds that decaying process rewards significantly boost performance on the long‑horizon Fine Cut task, while offering modest gains on shorter tasks; however, fixed process‑reward weighting can lead to reward‑hacking and degraded outcomes, and on‑policy self‑distillation provides limited benefit.
By Lei Zhao, Zongheng Cao, Xinyu Hu
The paper introduces Outcome-Conditioned Visual Credit (OVC), a plug‑in framework for multimodal reinforcement learning that separates visual association measurement from token‑level credit assignment. OVC uses Relative Visual Encoding to create a context‑relative visual coordinate and Outcome‑Conditioned Evidence Calibration to adjust token contributions based on outcome‑discriminative evidence. When applied to GSPO-token, OVC improves mean accuracy by 1.86–2.39 percentage points across six benchmarks without requiring additional reward models or inference‑time modules.
By Yuhang Han, Dingning Liu, Yujie Chen, Yaojie Zhang, Jingwei Song, Zequn Yang, Xuanming Shang, Li Ding, Zhijiang Guo, Linfeng Zhang
arXiv:2610.08402v1 Announce Type: new
Abstract: Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates two r...
By Jiaju Chen, Min Yang, Jinghua Piao, Xiaochong Lan, Xu Xia, Xiangnan He, Yong Li
arXiv:2610.32222v1 Announce Type: new
Abstract: Reinforcement learning has gained attention for improving action-conditioned world models (ACWMs), as it enables reward-driven optimization of visual q...
By Shibo Hong, Siyu Ren, FengJiao Chen, Zhongyuan Peng, Wei Shi, Yixin Cao