Interactive tool-using agents must solve real-world tasks via multi-turn interaction with both humans and external environments, requiring dialogue state tracking, multi-step tool execution, while following complex instructions. Post-training such agents is challenging because synthesis for high-quality multi-turn tool-use data is difficult to scale, and reinforcement learning (RL) could face noisy signals caused by user simulation, leading to degraded training efficiency. We propose a unified framework that combines a self-evolving data agent with verifier-based RL. Our system, EigenData, is a hierarchical multi-agent engine that synthesizes tool-grounded dialogues together with executable per-instance checkers, and improves generation reliability via closed-loop self-evolving process that updates prompts and workflow. Building on the synthetic data, we develop an RL recipe that first fine-tunes the user model and then applies GRPO-style training with trajectory-level group-relative advantages and dynamic filtering, yielding consistent improvements beyond SFT. Evaluated on tau^2-bench, our best model reaches 73.0% pass^1 on Airline and 98.3% pass^1 on Telecom, matching or exceeding frontier models. Overall, our results suggest a scalable pathway for bootstrapping complex tool-using behaviors without expensive human annotation.

翻译：交互式工具使用智能体需通过与人及外部环境的多轮交互解决现实任务，这要求其具备对话状态追踪、多步骤工具执行能力，并能遵循复杂指令。此类智能体的训练后优化面临双重挑战：高质量多轮工具使用数据的合成难以规模化扩展，而强化学习（RL）可能因用户模拟产生的噪声信号导致训练效率下降。本文提出一个统一框架，将自演进数据智能体与基于验证器的强化学习相结合。我们的系统EigenData是一个分层多智能体引擎，能同步生成工具落地的对话实例及其可执行的逐实例检查器，并通过更新提示词与工作流的闭环自演进过程提升生成可靠性。基于合成数据，我们开发了一套强化学习方案：先对用户模型进行微调，再采用结合轨迹级组间相对优势与动态筛选的GRPO风格训练，实现了超越监督微调（SFT）的持续性能提升。在tau^2-bench基准测试中，我们的最优模型在Airline任务上达到73.0% pass^1，在Telecom任务上达到98.3% pass^1，性能匹配或超越了前沿模型。总体而言，本研究为无需昂贵人工标注即可实现复杂工具使用行为规模化构建提供了可行路径。