Commercial image editing requires product identity preservation, accurate text rendering, and user appeal alongside general editing quality. We present KwaiMind, an image editing system combining general capabilities with e-commerce specialization. An agent-based data engine maintains approximately 1.8 million high-qua...
Jun-Long Wu, Zi-Jun Li, Yu-Ting Hu et al.· 0 citations
Aligning video generative models to human preferences heavily relies on Reinforcement Learning (RL), which suffers from extensive computational overhead. Existing workflows typically treat RL and distillation as disconnected stages: applying RL before distillation incurs prohibitive computational costs, whereas applyin...
Jiu-Zhou Lin, Jun-Long Wu, Feilong Zuo et al.· 0 citations
While on-policy distillation (OPD) reduces exposure bias by training student language models on their own rollouts, early student errors in long-horizon agentic scenarios can lead to contexts unfamiliar to the teacher. To improve trajectory quality, recent work on agentic OPD introduces teacher intervention into traini...
Yuchen Xia, Qianguo Sun, Chao Song et al.· arXiv.org· 0 citations
Text editing in product posters entails inserting new text or replacing existing text while preserving product appearance, background content, and global composition. Despite recent progress in instruction-based image editing, general-purpose models remain unreliable in this setting: they often omit or incorrectly rend...
Honglie Wang, Jia Sun, Zijun Li et al.· 0 citations
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