构建16万条高质量工具使用数据，提升大模型推理能力
ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset
- 用多智能体模拟真实人机交互，生成带函数图的工具调用轨迹
- 通过逐轮过滤剔除错误步骤，避免训练中错误累积
- 适合研究工具调用、复杂任务推理的大模型开发者
近年来，大语言模型代理已能借助外部工具解决复杂现实问题。然而，高质量数据轨迹的匮乏仍制约着更强代理的发展。现有对话合成工作仅在轨迹层面验证正确性，可能忽略会传播的逐轮错误，影响模型性能。为此，我们提出ToolMind，一个大规模、高精度的工具代理数据集，包含160,000条合成数据实例，基于超过20,000个工具和200,000条增强开源数据生成。数据合成流程首先构建基于参数相关性的函数图，再通过多智能体框架模拟真实的用户-助手-工具交互。除轨迹级验证外，还引入细粒度的逐轮过滤机制，剔除错误或次优步骤，确保仅保留高质量推理轨迹。该方法有效缓解了训练中的错误放大问题，同时保留了自修正推理信号，对鲁棒工具使用学习至关重要。在多个基准测试中，基于ToolMind微调的模型显著优于基线。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents have developed rapidly in recent years to solve complex real-world problems using external tools. However, the scarcity of high-quality trajectories still hinders the development of stronger LLM agents. Most existing works on multi-turn dialogue synthesis validate correctness only at the trajectory level, which may overlook turn-level errors that can propagate during training and degrade model performance. To address these limitations, we introduce ToolMind, a large-scale, high-quality tool-agentic dataset with 160k synthetic data instances generated using over 20k tools and 200k augmented open-source data instances. Our data synthesis pipeline first constructs a function graph based on parameter correlations and then uses a multi-agent framework to simulate realistic user-assistant-tool interactions. Beyond trajectory-level validation, we employ fine-grained turn-level filtering to remove erroneous or suboptimal steps, ensuring that only high-quality reasoning traces are retained. This approach mitigates error amplification during training while preserving self-corrective reasoning signals essential for robust tool-use learning. Models fine-tuned on ToolMind show significant improvements over baselines on several benchmarks.
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