Abstract
Large language models (LLMs) have recently been extended beyond traditional text generation to serve as interactive agents capable of using external tools based on user intent. However, current LLM agents still show limited ability to handle goal-oriented queries, which require decomposing a high-level objective into multiple interdependent API calls with correct planning and execution. Current approaches mainly rely on zero-shot evaluation due to the absence of training data. While proprietary closed-source models such as GPT-4 demonstrate strong reasoning abilities, smaller open-source models struggle to perform complex tool use effectively. Thus, we propose a novel training framework GOAT, which enables fine-tuning of LLM agents in a human annotation-free setting. GOAT automatically constructs synthetic datasets of goal-oriented API execution tasks directly from given API documents, equipping models with the ability to reason over interdependent calls and generate coherent responses. Through extensive experiments, we show that GOAT-trained agents achieve state-of-the-art performance across multiple existing goal-oriented benchmarks. In addition, we introduce GOATBench, a new goal-oriented API execution benchmark, and demonstrate that agents trained with GOAT also excel in this setting. These results highlight GOAT as a practical path toward building robust open-source LLM agents capable of complex reasoning and tool use.
Abstract (translated)
近期，大型语言模型（LLMs）已从传统的文本生成扩展到能够根据用户意图使用外部工具的交互式代理。然而，当前的LLM代理在处理以目标为导向的查询时仍表现出有限的能力，这些查询需要将高层次的目标分解为多个相互依赖的API调用，并进行正确的计划和执行。目前的方法主要依靠零样本评估来完成这项任务，因为缺乏训练数据。虽然像GPT-4这样的专有闭源模型展示了强大的推理能力，但较小的开源模型在有效使用复杂工具方面仍面临困难。因此，我们提出了一种新的培训框架GOAT（Goal-Oriented API Task），该框架能够在无需人工注释的情况下对LLM代理进行微调。GOAT能够从给定的API文档中自动构建目标导向型API执行任务的合成数据集，从而使模型具备处理相互依赖调用和生成连贯响应的能力。 通过广泛的实验，我们展示了使用GOAT训练的代理在多个现有的以目标为导向的基准测试中实现了最先进的性能。此外，我们还引入了GOATBench，这是一个新的以目标为导向的API执行基准，并证明了使用GOAT训练的代理在这个设置下同样表现出色。这些结果强调了GOAT是构建具有复杂推理和工具使用能力的强大开源LLM代理的实际路径。
URL
https://arxiv.org/abs/2510.12218
