APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated
  Agent-Human Interplay
Authors: name: Akshara Prabhakar, name: Zuxin Liu, name: Weiran Yao, name: Jianguo Zhang, name: Ming Zhu, name: Shiyu Wang, name: Zhiwei Liu, name: Tulika Awalgaonkar, name: Haolin Chen, name: Thai Hoang, name: Juan Carlos Niebles, name: Shelby Heinecke, name: Huan Wang, name: Silvio Savarese, name: Caiming Xiong

Training effective AI agents for multi-turn interactions requires
high-quality data that captures realistic human-agent dynamics, yet such data
is scarce and expensive to collect manually. We introduce APIGen-MT, a
two-phase framework that generates verifiable and diverse multi-turn agent
data. In the first phase, our agentic pipeline produces detailed task
blueprints with ground-truth actions, leveraging a committee of LLM reviewers
and iterative feedback loops. These blueprints are then transformed into
complete interaction trajectories through simulated human-agent interplay. We
train a family of models -- the xLAM-2-fc-r series with sizes ranging from 1B
to 70B parameters. Our models outperform frontier models such as GPT-4o and
Claude 3.5 on tau-bench and BFCL benchmarks, with the smaller models
surpassing their larger counterparts, particularly in multi-turn settings,
while maintaining superior consistency across multiple trials. Comprehensive
experiments demonstrate that our verified blueprint-to-details approach yields
high-quality training data, enabling the development of more reliable,
efficient, and capable agents. We open-source both the synthetic data collected
and the trained xLAM-2-fc-r models to advance research in AI agents. Models are
available on HuggingFace at
https://huggingface.co/collections/Salesforce/xlam-2-67ef5be12949d8dcdae354c4
and project website is https://apigen-mt.github.io