ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction
This addresses the bottleneck of simulation-based data generation for agentic task-solving with LLMs, offering a new paradigm for tool-augmented scenarios.
The paper tackles the problem of costly autoregressive data generation for multi-turn agentic dialogues by proposing ToolACE-MT, a non-autoregressive iterative framework that generates high-quality conversational trajectories through initialization, refinement, and verification stages, resulting in efficient and effective data construction.
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 limiting real-world performance of agentic tasks. In this paper, we propose a novel Non-Autoregressive Iterative Generation framework, called ToolACE-MT, 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.