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

TL;DR

ToolMind is a large-scale, high-quality dataset designed to enhance reasoning and tool use in LLM agents, constructed through a multi-agent simulation and fine-grained filtering to improve training and performance.

Contribution

We introduce ToolMind, a novel synthetic dataset with turn-level validation for training more robust and accurate LLM tool-use agents.

Findings

Models trained on ToolMind outperform baselines on multiple benchmarks.

High-quality, turn-level filtering reduces errors and improves reasoning.

Synthetic data generation using multi-agent simulation enhances dataset realism.

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.