title: APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated
    Agent-Human Interplay
summary: 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
paper_authors._id: 67f36505e11bd4b05579afbf
paper_authors.name: Akshara Prabhakar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:53:00.880Z
paper_authors._id: 6475718de0b188d3cb25f346
paper_authors.avatarUrl: /avatars/b0cdca7e19bbc1dbac00ea664108b51e.svg
paper_authors.fullname: Akshara Prabhakar
paper_authors.type: user
paper_authors.user: aksh555
paper_authors._id: 67f36505e11bd4b05579afc0
paper_authors.name: Zuxin Liu
paper_authors._id: 67f36505e11bd4b05579afc1
paper_authors.name: Weiran Yao
paper_authors._id: 67f36505e11bd4b05579afc2
paper_authors.name: Jianguo Zhang
paper_authors._id: 67f36505e11bd4b05579afc3
paper_authors.name: Ming Zhu
paper_authors._id: 67f36505e11bd4b05579afc4
paper_authors.name: Shiyu Wang
paper_authors._id: 67f36505e11bd4b05579afc5
paper_authors.name: Zhiwei Liu
paper_authors._id: 67f36505e11bd4b05579afc6
paper_authors.name: Tulika Awalgaonkar
paper_authors._id: 67f36505e11bd4b05579afc7
paper_authors.name: Haolin Chen
paper_authors._id: 67f36505e11bd4b05579afc8
paper_authors.name: Thai Hoang
paper_authors._id: 67f36505e11bd4b05579afc9
paper_authors.name: Juan Carlos Niebles
paper_authors._id: 67f36505e11bd4b05579afca
paper_authors.name: Shelby Heinecke
paper_authors._id: 67f36505e11bd4b05579afcb
paper_authors.name: Huan Wang
paper_authors._id: 67f36505e11bd4b05579afcc
paper_authors.name: Silvio Savarese
paper_authors._id: 67f36505e11bd4b05579afcd
paper_authors.name: Caiming Xiong
paper_title: APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated
    Agent-Human Interplay
paper_summary: 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
paper_ai_summary: The APIGen-MT framework generates high-quality multi-turn agent data using detailed task blueprints and simulated human-agent interactions, leading to superior model performance and consistency.
paper_ai_keywords: APIGen-MT agentic pipeline task blueprints ground-truth actions LLM reviewers iterative feedback loops interaction trajectories xLAM-2-fc-r model training $\tau$-bench BFCL benchmarks multi-turn settings synthetic data HuggingFace