APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated
  Agent-Human Interplay
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
67f36505e11bd4b05579afbf
Akshara Prabhakar
claimed_verified
2025-04-08T06:53:00.880Z
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/avatars/b0cdca7e19bbc1dbac00ea664108b51e.svg
Akshara Prabhakar
user
aksh555
67f36505e11bd4b05579afc0
Zuxin Liu
67f36505e11bd4b05579afc1
Weiran Yao
67f36505e11bd4b05579afc2
Jianguo Zhang
67f36505e11bd4b05579afc3
Ming Zhu
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Shiyu Wang
67f36505e11bd4b05579afc5
Zhiwei Liu
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Tulika Awalgaonkar
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Haolin Chen
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Thai Hoang
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Juan Carlos Niebles
67f36505e11bd4b05579afca
Shelby Heinecke
67f36505e11bd4b05579afcb
Huan Wang
67f36505e11bd4b05579afcc
Silvio Savarese
67f36505e11bd4b05579afcd
Caiming Xiong
APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated
  Agent-Human Interplay
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
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.
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