Reinforcing Diffusion Models by Direct Group Preference Optimization
Authors: name: Yihong Luo, name: Tianyang Hu, name: Jing Tang

While reinforcement learning methods such as Group Relative Preference
Optimization (GRPO) have significantly enhanced Large Language Models, adapting
them to diffusion models remains challenging. In particular, GRPO demands a
stochastic policy, yet the most cost-effective diffusion samplers are based on
deterministic ODEs. Recent work addresses this issue by using inefficient
SDE-based samplers to induce stochasticity, but this reliance on model-agnostic
Gaussian noise leads to slow convergence. To resolve this conflict, we propose
Direct Group Preference Optimization (DGPO), a new online RL algorithm that
dispenses with the policy-gradient framework entirely. DGPO learns directly
from group-level preferences, which utilize relative information of samples
within groups. This design eliminates the need for inefficient stochastic
policies, unlocking the use of efficient deterministic ODE samplers and faster
training. Extensive results show that DGPO trains around 20 times faster than
existing state-of-the-art methods and achieves superior performance on both
in-domain and out-of-domain reward metrics. Code is available at
https://github.com/Luo-Yihong/DGPO.