ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset

• URL: http://arxiv.org/abs/2511.15718v1
• Date: Wed, 12 Nov 2025 13:01:23 GMT
• Title: ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset
• Authors: Chen Yang, Ran Le, Yun Xing, Zhenwei An, Zongchao Chen, Wayne Xin Zhao, Yang Song, Tao Zhang,
• Abstract summary: We introduce ToolMind, a high-quality tool-agentic dataset with 160k synthetic data instances.<n>We employ fine-grained turn-level filtering to remove erroneous or suboptimal steps.<n>Models fine-tuned on ToolMind show significant improvements over baselines on several benchmarks.
• Score: 43.45582911794623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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