Scaling Laws for Tool-Use Agents via Controllable Environment Synthesis 待解读
新用户看这篇论文该怎么开始
- 先点「赞助解读」，AI 会把论文转成可直接执行的行动清单。
- 看完“可执行改进行动”后，可快速决定是否值得立项。
- 用上面的卡片内容直接发给团队，减少重复阅读。
摘要
We explore how to build performant large language model (LLM) tool-use agents that complete complex, multi-step tasks. Specifically, we study how data composition in synthetically generated tool-use environments affects the training of such agents. To do so, we propose the SynthAPI pipeline, which explicitly controls the diversity of generated environments along the axes of tools, tasks, and the databases on which the tools operate. By systematically varying SynthAPI's generation controls in 458,752 tasks across 849 trained models of four different sizes, we derive a scaling law that predicts downstream tool-use agent generalization based on the scale and composition of the synthesized environments. These predictable relationships reveal several lessons for scaling tool-use agent data. As data budgets grow, scaling tool and database diversity becomes more effective than increasing the number of tasks, and increasing data complexity yields larger gains. Scaling according to these findings yields agents that outperform models trained on data from prior work in large-scale tool-use agent and synthetic environment generation across four evaluation benchmarks.
分析报告
暂无报告。点击“分析”开始生成。
个性化解读 与社区共享解读不同
用自己的话告诉 AI 你想要什么样的解读（比如"用大白话讲给非专业人士听"、"重点分析对我们团队 RAG 系统的可迁移性"），生成一份只属于你自己的版本；生成后也可以选择设为"愿意共享"，被更多人看到、点赞。