@inproceedings{
liu2024graph,
title={Graph Diffusion Policy Optimization},
author={Yijing Liu and Chao Du and Tianyu Pang and Chongxuan Li and Min Lin and Wei Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8ohsbxw7q8}
}
Recent research has made significant progress in optimizing diffusion models for downstream objectives, which is an important pursuit in fields such as graph generation for drug design. However, directly applying these models to graph 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.
Graph Diffusion Policy Optimization
Yijing Liu Chao Du Tianyu Pang Chongxuan Li Min Lin Wei Chen