Dynamic Search for Inference-Time Alignment in Diffusion Models
from Best AI papers explained Hosted by Enoch H. Kang
This paper highlights the challenge of aligning diffusion models with desired outcomes by optimizing reward functions, especially when gradient information is unavailable. The core contribution is the proposal of DSearch, a novel gradient-free method that reframes this alignment as a search problem on a dynamically constructed tree representing the diffusion process. DSearch utilizes heuristic functions and dynamic scheduling to efficiently explore the search space and identify high-reward samples. Experimental results across image generation, biological sequence design, and molecular optimization tasks demonstrate DSearch's effectiveness in balancing reward maximization, sample quality, and diversity.
Embed this episode
No transcript yet.