User-Oriented Multi-Turn Dialogue Generation with Tool Use at scale

2026-01-13

AI Summary

This work addresses the limitations of existing datasets and generation methods, which rely on static tool sets and struggle to simulate high-complexity, multi-turn human-agent collaboration in open-ended scenarios. To overcome this, the authors propose a user-oriented multi-turn dialogue generation framework that decouples task execution from user simulation, dynamically modeling human users’ progressive requests and turn-by-turn feedback. Built upon a large reasoning model (LRM), the framework enables plug-and-play generation, arbitrary state initialization, and execution of multiple tasks within a single dialogue trajectory, substantially enhancing data density and realism. The resulting dialogue dataset significantly outperforms those generated by conventional task-oriented approaches in terms of conversation length, task complexity, and interaction authenticity.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The recent paradigm shift toward large reasoning models (LRMs) as autonomous agents has intensified the demand for sophisticated, multi-turn tool-use capabilities. Yet, existing datasets and data-generation approaches are limited by static, predefined toolsets that cannot scale to the complexity of open-ended human-agent collaboration. To address this, we initially developed a framework for automated task-oriented multi-turn dialogue generation at scale, utilizing an LRM-based simulator to dynamically generate high-value, domain-specific tools to solve specified tasks. However, we observe that a purely task-oriented design often results in"solely task-solving"trajectories, where the agent completes the objective with minimal interaction, failing to generate the high turn-count conversations seen in realistic scenarios. To bridge this gap, we shift toward a user-oriented simulation paradigm. By decoupling task generation from a dedicated user simulator that mimics human behavioral rules - such as incremental request-making and turn-by-turn feedback - we facilitate more authentic, extended multi-turn dialogues that reflect the iterative nature of real-world problem solving. Our generation pipeline operates as a versatile, plug-and-play module capable of initiating generation from any state, ensuring high scalability in producing extended tool-use data. Furthermore, by facilitating multiple task completions within a single trajectory, it yields a high-density dataset that reflects the multifaceted demands of real-world human-agent interaction.

Problem

• multi-turn dialogue
• tool use
• user-oriented simulation
• dialogue generation
• human-agent collaboration

Innovation

• user-oriented dialogue generation
• large reasoning models
• dynamic tool use
• multi-turn conversation simulation
• scalable agent interaction