Offline black-box optimisation presents a significant challenge in fields ranging from DNA sequencing to robotics, requiring the identification of optimal solutions from pre-existing datasets. Ye Yuan, Can Chen (from MILA – Quebec AI Institute), and Zipeng Sun (from McGill, MILA – Quebec AI Institute), alongside Dinghuai Zhang and Christopher Pal (from Polytechnique Montreal, Canada CIFAR AI Chair), demonstrate a novel approach utilising diffusion large language models (dLLMs) to overcome limitations of current methods. Their research addresses the difficulty traditional techniques have with capturing bidirectional dependencies within complex designs, instead harnessing the iterative refinement and bidirectional modelling capabilities of diffusion LLMs. By…