One potential limitation of the SVDD approach is its inherent reliance on the pre-trained diffusion models, which are designed to capture the natural design space of the training data. As a result, the samples generated by SVDD are likely to remain close to the distribution of the training data, potentially limiting the exploration of novel designs that deviate significantly from this distribution. This proximity to the pre-trained model acts as a regularization mechanism, which, while beneficial for maintaining naturalness, may hinder the discovery of innovative or unconventional designs that could be valuable in applications such as drug discovery or creative content generation.
Additionally, the algorithm's performance is contingent upon the quality and diversity of the pre-trained model. If the pre-trained model lacks representation of certain design spaces or is biased towards specific types of solutions, the SVDD algorithm may struggle to generate diverse outputs. Furthermore, the optimization of reward functions that are learned from offline data can lead to overfitting, where the model becomes too specialized in optimizing for known rewards, further constraining its ability to explore uncharted territories in the design space.