V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think
Aligning denoising generative models with human preferences or verifiable rewards remains a key challenge. While policy-gradient online reinforcement learning (RL) offers a principled post-training framework, its direct application is hindered by the intractable likelihoods of these models. Prior work therefore either optimizes an induced Markov decision process (MDP) over sampling trajectories, which is stable but inefficient, or uses likelihood surrogates based on the diffusion evidence lower bound (ELBO), which have so far underperformed on visual generation. Our key insight is that the ELBO-based approach can, in fact, be made both stable and efficient. By reducing surrogate variance ...
| Authors | Bingda Tang, Yuhui Zhang, Xiaohan Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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