User-Oriented Multi-Turn Dialogue Generation with Tool Use at scale
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.
6967202cc5e371f6b235d1cc
Jungho Cho
claimed_verified
2026-01-15T15:04:06.076Z
6596a87480a4560b8f9b9532
/avatars/33f39ee01c1648f1daca41a40a4964fb.svg
Christopher Cho
ChristopherCho
user
ChristopherCho
6967202cc5e371f6b235d1cd
Minbyul Jeong
6967202cc5e371f6b235d1ce
Sungrae Park
admin_assigned
2026-01-14T12:49:29.339Z
65446c938737c799e9ad6f83
/avatars/6ade251e01442b14cbf8cd7888358fd1.svg
Sungrae Park
sungrae-park
user
sungrae-park
User-Oriented Multi-Turn Dialogue Generation with Tool Use at scale
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.
Large reasoning models enable scalable multi-turn dialogue generation through automated task-oriented simulation and user-oriented behavioral modeling for enhanced human-agent interaction datasets.
large reasoning models multi-turn dialogue generation task-oriented simulation user simulator behavioral rules turn-by-turn feedback automated task generation high-density dataset human-agent interaction