分析大模型如何优化搜索，发现高效模型靠持续微调而非突变。
What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search
- 通过轨迹分析发现强模型会逐步局部优化，弱模型则频繁偏离方向。
- 15个模型在8个任务上表现差异大，初始能力相似但最终结果悬殊。
- 搜索局部化比追求新颖更重要，过度创新反而导致停滞。
近期研究展示了将大语言模型（LLMs）融入进化与代理优化系统的潜力。然而，驱动这些优化效果的机制仍不明确。本文对LLM引导的进化搜索进行了大规模研究，收集了15个LLMs在8个任务上的优化轨迹。尽管零样本求解能力与最终优化结果相关，但仅解释部分方差：初始能力相近的模型往往产生截然不同的搜索轨迹与结果。通过轨迹分析发现，强优化器表现为局部精炼者，在语义空间中持续产生小幅改进并逐步聚焦；而弱优化器则表现出大幅语义漂移，伴随偶发突破后迅速停滞。值得注意的是，多种解的新颖性度量无法预测最终性能；新颖性仅在搜索保持在高性能区域附近时才有效。结果强调了轨迹分析对理解与改进基于LLM的优化系统的重要性，并为设计与训练提供了可操作洞见。
原文摘要 · Abstract (English)
Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, the mechanisms driving these optimization gains remain poorly understood. In this work, we present a large-scale study of LLM-guided evolutionary search, collecting optimization trajectories for 15 LLMs across 8 tasks. Although zero-shot problem-solving ability correlates with final optimization outcomes, it explains only part of the variance: models with similar initial capability often induce dramatically different search trajectories and outcomes. By analyzing these trajectories, we find that strong LLM optimizers behave as local refiners, producing frequent incremental improvements while progressively localizing the search in semantic space. Conversely, weaker optimizers exhibit large semantic drift, with sporadic breakthroughs followed by stagnation. Notably, various measures of solution novelty do not predict final performance; novelty is beneficial only when the search remains sufficiently localized around high-performing regions of the solution space. Our results highlight the importance of trajectory analysis for understanding and improving LLM-based optimization systems and provide actionable insights for their design and training.
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