ICML 2025

Diversity By Design: Leveraging Distribution Matching
for Offline Model-Based Optimization
Michael S. Yao 1 James C. Gee 1 Osbert Bastani 1
Abstract
The goal of offline model-based optimization
(MBO) is to propose new designs that maximize
a reward function given only an offline dataset.
However, an important desiderata is to also pro-
pose a diverse set of final candidates that capture
many optimal and near-optimal design config-
urations. We propose Diversity in Adversarial
Model-based Optimization (DynAMO) as a
novel method to introduce design diversity as an
explicit objective into any MBO problem. Our
key insight is to formulate diversity as a distribu-
tion matching problem where the distribution of
generated designs captures the inherent diversity
contained within the offline dataset. Extensive
experiments spanning multiple scientific domains
show that DynAMO can be used with common
optimization methods to significantly improve the
diversity of proposed designs while still discover-
ing high-quality candidates.
1. Introduction
Discovering designs that optimize certain desirable proper-
ties is a ubiquitous task that spans a wide range of scientific
and engineering domains. For example, we might seek
to design a drug with the most potent therapeutic efficacy
(Brown et al., 2019; Kong et al., 2023; Du et al., 2024); build
a robot that is most capable of navigating complex environ-
ments (Ahn et al., 2020; Trabucco et al., 2021; Wang et al.,
2023); or engineer a material with a certain desirable prop-
erty (Stanev et al., 2018; Pogue et al., 2023; Gashmard et al.,
2024; Ma et al., 2024). However, experimentally validating
every proposed design can be expensive, time-intensive, or
even impossible in many applications. These limitations
can preclude the use of conventional ‘online’ optimization
methods for such generative design tasks.
1University of Pennsylvania, Philadelphia, PA, USA. Corre-
spondence to: Michael Yao <myao2199@seas.upenn.edu>.
Proceedings of the...