用强化学习优化大模型搜索的算力分配，提升结果稳定性和性能。
Compute Allocation in Evolutionary Search: From Depth-Breadth to Multi-Armed Bandits
- 基于多臂赌博机动态分配计算资源到不同搜索路径
- 平均性能提升12.3%，高波动场景下可靠性显著增强
- 适用于需要稳定高效推理的数学与组合任务
LLM引导的进化搜索（Evolve系统）在数学和组合任务上达到顶尖水平，但多数系统仅报告多次运行中的最佳结果，未记录运行间分布。我们研究在固定LLM调用预算下如何分配计算资源，并评估单次运行能否可靠达到报告性能。通过对五种模型和三种任务进行深度-广度网格扫描，我们发现两个经验规律：一是能力排序在有效FLOPs上趋于收敛；二是深度-广度拟合呈双线性且受任务-模型能力交互影响。基于此，我们提出BaSE（基于赌博机的自演化），一种在不改变模型、提示或评估器的前提下，跨并行轨迹动态分配LLM调用的算法。在8个（模型, 任务）组合中，相比最强的岛屿协议基线，平均适应度提升12.3%，尤其在高方差设置下获显著可靠性增益。
原文摘要 · Abstract (English)
LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocumented. We ask how a fixed budget of LLM calls should be allocated, and how reliably a single run reaches the reported numbers. Sweeping the depth-breadth grid over five models and three tasks, we identify two empirical regularities: a fitness-compute envelope along which capability ordering largely collapses on effective FLOPs, and a bilinear depth-breadth fit with task-specific interaction; both are gated by model-task capability. Motivated by these regularities, we propose BaSE (Bandit-based Self-Evolving), a multi-armed bandit that allocates LLM calls across parallel trajectories. Without changing the model, prompt, or evaluator, BaSE improves mean fitness by 12.3% over the strongest island-protocol baseline across 8 (model, task) cells, with the largest gains on high-variance settings: a reliability gain from allocation alone.
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