Quality-Diversity (QD) approaches are a promising direction to develop
open-ended processes as they can discover archives of high-quality solutions
across diverse niches. While already successful in many applications, QD
approaches usually rely on combining only one or two solutions to generate new
candidate solutions. As observed in open-ended processes such as technological
evolution, wisely combining large diversity of these solutions could lead to
more innovative solutions and potentially boost the productivity of QD search.
In this work, we propose to exploit the pattern-matching capabilities of
generative models to enable such efficient solution combinations. We introduce
In-context QD, a framework of techniques that aim to elicit the in-context
capabilities of pre-trained Large Language Models (LLMs) to generate
interesting solutions using the QD archive as context. Applied to a series of
common QD domains, In-context QD displays promising results compared to both QD
baselines and similar strategies developed for single-objective optimization.
Additionally, this result holds across multiple values of parameter sizes and
archive population sizes, as well as across domains with distinct
characteristics from BBO functions to policy search. Finally, we perform an
extensive ablation that highlights the key prompt design considerations that
encourage the generation of promising solutions for QD.

通过利用生成模型的模式匹配能力，我们提出了 In-context QD 框架，通过将 QD 档案作为上下文，利用预训练的大型语言模型的上下文能力生成有趣的解决方案，在多个领域中展现出有希望的结果，并在 QD 搜索中取得了良好的效果。