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cs.LG2026
LC-GRPO: Bridging Train-Inference Gap for Flow-Based GRPO with Langevin Correction
Yingqing Guo, Hui Yuan, Zijian He +2
Flow-based generative models are typically sampled by solving a deterministic ordinary differential equation (ODE), whereas online reinforcement learning requires stochastic rollou…
cs.LG2025
TreeGRPO: Tree-Advantage GRPO for Online RL Post-Training of Diffusion Models
Zheng Ding, Weirui Ye
Reinforcement learning (RL) post-training is crucial for aligning generative models with human preferences, but its prohibitive computational cost remains a major barrier to widesp…