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.

翻译：质量-多样性（QD）方法是发展开放式过程的一个有前景方向，因为它能发现跨不同生态位的高质量解决方案档案库。尽管已在众多应用中取得成功，但QD方法通常仅依赖组合一个或两个解决方案来生成新的候选方案。正如在技术进化等开放式过程中所观察到的，明智地组合大量此类解决方案可能带来更具创新性的方案，并有可能提升QD搜索的效率。在本工作中，我们提出利用生成模型的模式匹配能力来实现此类高效的解决方案组合。我们引入了情境内QD（In-context QD）这一技术框架，旨在激发预训练大语言模型（LLM）的情境内能力，以QD档案库为上下文生成有趣的解决方案。在系列常见QD领域中的应用表明，与QD基线方法及为单目标优化开发的类似策略相比，情境内QD展现了令人期待的结果。此外，这一结论在参数规模和档案种群规模的不同取值下，以及从BBO函数到策略搜索等具有不同特征的领域中均保持成立。最后，我们进行了广泛的消融实验，揭示了促进QD生成有前景方案的关键提示设计考量。