ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction

TL;DR

ToolACE-MT introduces a non-autoregressive, multi-stage framework for efficient and high-quality multi-turn agentic dialogue generation, reducing reliance on costly autoregressive methods and enhancing data construction for tool-augmented LLMs.

Contribution

It presents a novel non-autoregressive iterative generation approach with three stages, improving efficiency and quality in agentic multi-turn dialogue data creation.

Findings

Enables efficient multi-turn dialogue generation

Produces high-quality, coherent dialogues

Reduces computational costs compared to autoregressive methods

Abstract

Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions, often involving complex function calls and dynamic user-agent exchanges. Existing simulation-based data generation methods for such scenarios rely heavily on costly autoregressive interactions between multiple LLM agents, thereby compromising the practical efficiency of agentic data generation. In this paper, we propose ToolACE-MT, a novel Non-Autoregressive Iterative Generation framework for constructing high-quality multi-turn agentic dialogues. ToolACE-MT generates full conversational trajectories through three stages: coarse-grained initialization, iterative refinement, and offline verification. The initialization phase builds a structurally complete yet semantically coarse dialogue skeleton; the iterative refinement phase introduces realistic complexities and continued refinement via mask-and-fill operations; and the offline verification phase ensures correctness and coherence via rule- and model-based checks. Experiments demonstrate that ToolACE-MT enables efficient, effective and generalizable agentic data generation, offering a new paradigm for high-quality data construction in tool-augmented LLM scenarios.