Abstract
Large Language Model agents demonstrate potential in solving real-world problems via tools, yet generalist intelligence is bottlenecked by scarce high-quality, long-horizon data. Existing methods collect privacy-constrained API logs or generate scripted interactions lacking diversity, which struggle to produce data requisite for scaling capabilities. We propose AgentSkiller, a fully automated framework synthesizing multi-turn interaction data across realistic, semantically linked domains. It employs a DAG-based architecture with explicit state transitions to ensure determinism and recoverability. The pipeline builds a domain ontology and Person-Centric Entity Graph, defines tool interfaces via Service Blueprints for Model Context Protocol servers, and populates environments with consistent databases and strict Domain Policies. A cross-domain fusion mechanism links services to simulate complex tasks. Finally, the pipeline creates user tasks by verifying solution paths, filtering via execution-based validation, and generating queries using a Persona-based Simulator for automated rollout. This produces reliable environments with clear state changes. To demonstrate effectiveness, we synthesized $\approx$ 11K interaction samples; experimental results indicate that models trained on this dataset achieve significant improvements on function calling over baselines, particularly in larger parameter regimes.
Abstract (translated)
大型语言模型代理在通过工具解决现实世界问题方面展示了潜力，但通才智能的发展受限于高质量、长周期数据的稀缺。现有的方法收集受隐私限制的API日志或生成缺乏多样性的脚本化交互，难以产生扩大能力所需的数据。我们提出AgentSkiller，这是一个完全自动化的框架，用于在现实且语义关联的领域中合成多轮互动数据。 该框架采用基于有向无环图（DAG）架构，并通过明确的状态转换确保确定性和可恢复性。整个流程包括构建领域本体论和以人物为中心的实体图，定义工具接口时使用服务蓝图来为模型上下文协议服务器制定标准，并在环境中填充一致的数据库和严格的域策略。跨域融合机制链接服务以模拟复杂任务。最后，该管道通过验证解决方案路径、基于执行验证过滤并通过基于人物的仿真器生成查询来进行用户任务创建。这将产生具有明确状态变化的可靠环境。 为了证明其有效性，我们合成了约1.1万份交互样本；实验结果显示，在此数据集上训练的模型在功能调用方面相对于基线取得了显著改进，特别是在更大的参数范围内表现尤为突出。
URL
https://arxiv.org/abs/2602.09372
