Scalability of variable-length diffusion models beyond demonstrated domains

Determine whether variable-length diffusion models based on trans-dimensional jump diffusion, which approximate the addition of new elements during generation, scale effectively to state spaces more complex than those originally used in their demonstrations, assessing both modeling accuracy and practical performance under increased state-space complexity.

Background

The paper highlights that while diffusion and flow matching methods are effective for fixed-length continuous and discrete states, variable-length generation—especially in continuous or multimodal spaces—remains challenging. A notable prior approach, trans-dimensional jump diffusion, introduces elements via an approximation during generation.

The authors explicitly note uncertainty regarding how such variable-length diffusion models behave when extended to more complex state spaces than those used in the initial demonstrations, motivating a need to rigorously evaluate their scalability and fidelity in broader settings.