进化式单步生成器：用于离散设计的快速多样化采样

中文摘要 AI 辅助

若干离散设计任务（如分子发现）需要以低计算成本获得多样化的有用候选集合。仅高有效性并不能保证有用的候选库：重复生成相同的有效结构只会留下很少的不同备选方案。同时训练可行性和多样性具有挑战性，因为许多相关标准只能在硬解码之后进行评估。为解决这一挑战，我们提出了EGO（进化式单步推断生成器），一种直接在离散输出上训练紧凑生成器的框架。该方法将分布匹配与结构约束以及可选的多样性或历史依赖奖励相结合，使用对偶低秩进化策略，无需特定标准的可微替代。一旦训练完成，生成器可在单次神经网络评估中产生整个图。在分子生成基准上，我们的紧凑生成器相比近期单步流映射基线，在每估计密集操作上的有效且唯一产出量超过50倍，同时保持高化学有效性。在骨架补全中，EGO在生成SMILES方面实现了比MoLeR快44.3倍的观测加速，并在匹配的时间预算内生成和筛选时产生约10倍多的过滤通过提案。在化学之外，EGO在NAS-Bench-101上产生比松弛梯度训练多1.54倍的不同保留精英架构。低生成成本可能实现跨离散设计任务的实时候选生成，支持受限设计空间的交互式探索和下游评估候选集的快速构建。

英文摘要

Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity alone does not guarantee a useful candidate library: repeatedly generating the same valid structures leaves few distinct alternatives. Training for both feasibility and diversity is challenging because many relevant criteria can only be evaluated after hard decoding. To address this challenge, we propose EGO (Evolutionary Generators with One-step inference), a framework for training compact generators directly on discrete outputs. The method combines distribution matching with structural constraints and optional diversity or history-dependent rewards, using antithetic low-rank evolution strategies without requiring criterion-specific differentiable surrogates. Once trained, the generator produces the entire graph in a single neural-network evaluation. On molecular generation benchmarks, our compact generator achieves over $50\times$ the valid-and-unique yield per estimated dense operation compared to recent one-step flow-map baselines while retaining high chemical validity. In scaffold completion, EGO achieves an observed $44.3\times$ speedup over MoLeR in generation to SMILES and produces approximately $10\times$ as many filter-passing proposals within matched time budgets for generation and screening. Beyond chemistry, EGO produces $1.54\times$ as many distinct held-out elite architectures as relaxed gradient training on NAS-Bench-101. The low generation cost may enable real-time candidate generation across discrete design tasks, supporting interactive exploration of constrained design spaces and rapid construction of candidate sets for downstream evaluation.

发表机构

• University of Trento（特伦托大学）
• UiT The Arctic University of Norway（挪威北极圈大学）

机构由 AI 辅助整理，请以论文原文为准。