APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
$\tau$-benchagentic pipelineapigen-mtbfcl benchmarkscapable agentsefficient agentsground-truth actionsinteraction trajectoriesiterative feedback loopsllm reviewersmulti-turn interactionsreliable agentssynthetic datatask blueprintsverified blueprint-to-details approachxlam-2-fc-r models
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