ToolRACER generates 5.6K conversation trajectories with 66% failure-prone scenarios via three-agent emulation and four-stage validation, lifting fine-tuned Qwen3-4B to 39.20% macro Pass@1 on τ-bench
The authors present ToolRACER, a three-stage synthetic data pipeline coordinating user, assistant, and tool emulation agents that injects user-originated and environment-originated adversarial behaviors, validates traces through four evaluators plus a refinement loop, and yields ToolRACERBench: six domains, 55 personas, 5.6K trajectories with about 66% failure-prone scenarios; models fine-tuned on it improve end-to-end agentic accuracy on τ-bench, BFCLv3, and ACEBench over baseline training sets.