Zipeng Sun

Doctorat - McGill

Superviseur⋅e principal⋅e

Xue (Steve) Liu

Sujets de recherche

Apprentissage par renforcement

Grands modèles de langage (LLM)

Modèles de diffusion

Raisonnement

Traitement du langage naturel

Publications

Diffusion Large Language Models for Black-Box Optimization

Can Chen

Offline black-box optimization (BBO) aims to find optimal designs based solely on an offline dataset of designs and their labels. Such scena… (voir plus)rios frequently arise in domains like DNA sequence design and robotics, where only a few labeled data points are available. Traditional methods typically rely on task-specific proxy or generative models, overlooking the in-context learning capabilities of pre-trained large language models (LLMs). Recent efforts have adapted autoregressive LLMs to BBO by framing task descriptions and offline datasets as natural language prompts, enabling direct design generation. However, these designs often contain bidirectional dependencies, which left-to-right models struggle to capture. In this paper, we explore diffusion LLMs for BBO, leveraging their bidirectional modeling and iterative refinement capabilities. This motivates our in-context denoising module: we condition the diffusion LLM on the task description and the offline dataset, both formatted in natural language, and prompt it to denoise masked designs into improved candidates. To guide the generation toward high-performing designs, we introduce masked diffusion tree search, which casts the denoising process as a step-wise Monte Carlo Tree Search that dynamically balances exploration and exploitation. Each node represents a partially masked design, each denoising step is an action, and candidates are evaluated via expected improvement under a Gaussian Process trained on the offline dataset. Our method, dLLM, achieves state-of-the-art results in few-shot settings on design-bench.

2026-01-19

ArXiv (prépublication)

doi.org

arxiv.org

Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generation

Senyu Li

Zipeng Sun

Jiayi Wang

Xue (Steve) Liu

Pontus Stenetorp

Siva Reddy

David Ifeoluwa Adelani