APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay

Akshara Prabhakar, Zuxin Liu, Ming Zhu, Jianguo Zhang, Tulika Manoj Awalgaonkar, Shiyu Wang, Zhiwei Liu, Haolin Chen, Thai Quoc Hoang, Juan Carlos Niebles, Shelby Heinecke, Weiran Yao, Huan Wang, Silvio Savarese, Caiming Xiong

Keywords: language agent, function-calling, synthetic data generation

TL;DR: An agentic pipeline for multi-turn synthetic data generation that produces high-quality training data for AI agents.

Abstract:

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.

Dataset: https://huggingface.co/datasets/Salesforce/APIGen-MT-5k &
Models: https://huggingface.co/collections/Salesforce/xlam-2-67ef5be12949d8dcdae354c4

Dataset URL: https://huggingface.co/datasets/Salesforce/APIGen-MT-5k

Code URL: https://anonymous.4open.science/r/apigen-mt_eval-0281

Primary Area: Evaluation (e.g., data collection methodology, data processing methodology, data analysis methodology, meta studies on data sources, extracting signals from data, replicability of data collection and data analysis and validity of metrics, validity of data collection experiments, human-in-the-loop for data collection, human-in-the-loop for data evaluation)

Submission Number: 1057