Yes, the SVDD framework could be adapted to work with other types of generative models, such as variational autoencoders (VAEs) or generative adversarial networks (GANs). The core principles of SVDD, which involve optimizing downstream reward functions without the need for fine-tuning or differentiable proxy models, can be applied to these generative architectures with some modifications.
For VAEs, the SVDD approach could leverage the latent space representation learned by the VAE to sample from a distribution that maximizes the expected reward. The soft value functions could be defined in the context of the VAE's latent space, guiding the sampling process to explore regions that yield high rewards while maintaining the generative model's ability to produce realistic samples.
In the case of GANs, the SVDD framework could be integrated into the generator's sampling process. The generator could produce multiple samples, and the soft value functions could evaluate these samples based on the reward feedback. The selection of samples could then be influenced by the value functions, similar to the SVDD approach, allowing for the generation of high-reward outputs while still adhering to the distribution learned by the GAN.
Overall, the adaptability of the SVDD framework to various generative models highlights its versatility and potential for broader applications in optimizing complex design problems across different domains.