  Recent research has made significant progress in optimizing diffusion models
for specific downstream objectives, which is an important pursuit in fields
such as graph generation for drug design. However, directly applying these
models to graph diffusion presents challenges, resulting in suboptimal
performance. This paper introduces graph diffusion policy optimization (GDPO),
a novel approach to optimize graph diffusion models for arbitrary (e.g.,
non-differentiable) objectives using reinforcement learning. GDPO is based on
an eager policy gradient tailored for graph diffusion models, developed through
meticulous analysis and promising improved performance. Experimental results
show that GDPO achieves state-of-the-art performance in various graph
generation tasks with complex and diverse objectives. Code is available at
https://github.com/sail-sg/GDPO.
