用低维隐空间高效优化生成模型输出，提升目标样本质量。
Sample-Efficient Optimisation over the Outputs of Generative Models
- 构建生成模型的代理隐空间，实现无训练的低维表示
- 在图像与蛋白质设计中找到评分显著更高的样本
- 无需重训练，兼容各类模型与优化器，效率高
现代生成式AI模型（如扩散模型和流匹配模型）可从丰富数据分布中采样。然而，科学与工程中的许多应用不仅需要采样，还需在分布内搜索满足特定任务指标的最优样本。本文提出O3（Optimisation Over the Outputs of Generative Models），一种针对连续变量扩散与流匹配模型的样本高效黑箱优化方法。O3基于代理隐空间：无需额外训练即可从生成模型中提取的低维欧氏嵌入。该表示具有可控维度，可直接应用标准优化算法。在图像与蛋白质设计任务中，代理空间优化所找到的样本评分显著高于标准采样或原始隐空间优化。该方法对模型和优化器均无依赖，生成开销几乎不变，且无需对生成模型进行再训练或微调。
原文摘要 · Abstract (English)
Modern generative AI models, such as diffusion and flow matching models, can sample from rich data distributions. However, many applications, especially in science and engineering, require more than drawing samples from the model distribution: they require searching within this distribution for samples that optimise task-specific criteria. In this work, we propose O3 (Optimisation Over the Outputs of Generative Models), a method for sample-efficient black-box optimisation over continuous-variable diffusion and flow-matching models. O3 is built around surrogate latent spaces: low-dimensional Euclidean embeddings that can be extracted from a generative model without additional training. The resulting representations have controllable dimensionality and support the direct application of standard optimisation algorithms. We show, on image and protein design tasks, that surrogate-space optimisation finds substantially higher-scoring samples than standard sampling or optimisation in the original latent space. Our method is model- and optimiser-agnostic, incurs negligible additional cost over standard generation, and requires no retraining or fine-tuning of the generative model.
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