SynthAPI 管线提出工具使用智能体的 Scaling Law
Scaling Laws for Tool-Use Agents via Controllable Environment Synthesis
训练了 849 个模型才总结出的规律：做工具调用智能体时，堆工具和数据库种类比堆任务数量更划算，做数据的建议看看。
这篇论文提出 SynthAPI 管线，沿工具、任务和数据库三个维度控制合成环境的多样性。团队在 458,752 个任务上训练了 849 个四种规模的模型，推导出可预测工具使用智能体泛化能力的 scaling law。结果显示，随数据预算增长，扩大工具和数据库多样性比增加任务数量更有效，提升数据复杂度带来更大收益。按这些发现扩展数据后，智能体在四个评测基准上超过此前工作训练的模型。
Scaling Laws for Tool-Use Agents via Controllable Environment Synthesis
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.