将大任务拆解优化，让大模型代理系统更高效。
Divide, Optimize, Merge: Fine-Grained LLM Agent Optimization at Scale
- 把长训练轨迹拆成小块，逐块优化再逐步合并。
- 在多个基准上提升1.6%-8.6%，提示词用量减少56.3%。
- 适合需要大规模优化复杂智能体的开发者。
基于大模型的优化在提升智能体系统性能方面展现出巨大潜力。然而，传统方法在单次处理整个训练数据集轨迹时，随着数据集规模增大，会出现上下文窗口溢出和模式识别能力下降的问题。为此，我们提出细粒度优化（FGO）框架，将大规模优化任务分解为可管理的子集，进行针对性优化，并通过渐进式合并系统整合优化结果。在ALFWorld、LogisticsQA和GAIA基准上的评估表明，FGO相比现有方法性能提升1.6%-8.6%，同时平均提示词消耗降低56.3%。进一步分析显示，FGO在所有数据集规模下均实现最稳定的性能提升，验证了其可扩展性与高效性。
原文摘要 · Abstract (English)
LLM-based optimization has shown remarkable potential in enhancing agentic systems. However, the conventional approach of prompting LLM optimizer with the whole training trajectories on training dataset in a single pass becomes untenable as datasets grow, leading to context window overflow and degraded pattern recognition. To address these challenges, we propose Fine-Grained Optimization (FGO), a scalable framework that divides large optimization tasks into manageable subsets, performs targeted optimizations, and systematically combines optimized components through progressive merging. Evaluation across ALFWorld, LogisticsQA, and GAIA benchmarks demonstrate that FGO outperforms existing approaches by 1.6-8.6% while reducing average prompt token consumption by 56.3%. Our framework provides a practical solution for scaling up LLM-based optimization of increasingly sophisticated agent systems. Further analysis demonstrates that FGO achieves the most consistent performance gain in all training dataset sizes, showcasing its scalability and efficiency.
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