用非自回归方法高效生成多轮智能体对话数据
ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction
- 分三阶段生成：粗初始化、迭代细化、离线验证
- 生成效率远超传统自回归方法，且对话质量高
- 适合需要大量高质量多轮交互数据的研究者
大型语言模型在智能体任务求解中需进行多轮、多步骤交互，常涉及复杂函数调用和动态用户-代理交流。现有基于模拟的数据生成方法依赖多个智能体之间的昂贵自回归交互，严重降低数据生成的实际效率。本文提出ToolACE-MT，一种新型非自回归迭代生成框架，用于构建高质量的多轮智能体对话。该框架通过三个阶段生成完整对话轨迹：粗粒度初始化阶段构建结构完整但语义粗糙的对话骨架；迭代细化阶段通过掩码填充操作引入真实复杂性并持续优化；离线验证阶段采用规则与模型结合的方法确保对话正确性与连贯性。实验表明，ToolACE-MT实现了高效、有效且可泛化的智能体数据生成，为工具增强型大模型场景下的高质量数据构建提供了新范式。
原文摘要 · Abstract (English)
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.
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