MidTool为模型如何识别工具可用性、从上下文中定位论点、组成工具调用工作流程和从不完整信息中恢复提供了新方法，与基线相比，在中训练后性能显著提升，值得一试。
Mid-training阶段对大型语言模型能力塑造至关重要。MidTool是一个用于工具使用中训练数据合成的开放语料库构建管道，结合大规模网络、PDF和代码数据，以及来自真实工具API、MCP技能和基于文档的工作流程的合成监督。MidTool-Mix在中训练Qwen3-4B-Base和Qwen3-8B-Base后，在BFCL、tau2-Bench和MCP Universe上，与基线相比，MidTool-Mix在SFT和RL下均持续提升下游性能。这表明通用工具使用等LLM能力，像其他重要能力一样，从专门的Mid-training中受益，而不是完全依赖后训练。
MidTool: Mid-training Data Synthesis for Agentic Tool Use
Mid-training is increasingly recognized as a critical stage for shaping the capabilities of large language models. Recent work has shown that targeted mid-training can strengthen reasoning-intensive abilities such as math and science, and can also improve agentic capabilities in software-engineering settings. In this work, we study the parallel but less explored agentic capability: general tool use. We present MidTool, an open corpus construction pipeline for agentic tool-use mid-training that combines large-scale web, PDF, and code data with synthesized supervision from real-world tool APIs, MCP skills, and document-grounded workflows. MidTool is designed to teach models how to recognize tool affordances, ground arguments from context, compose tool call workflow, and recover from incomplete information. We mid-train Qwen3-4B-Base and Qwen3-8B-Base on MidTool-Mix, and then apply follow-up post-training with both supervised fine-tuning and reinforcement learning. Compared with baselines, MidTool-Mix consistently improves downstream performance under both SFT and RL on BFCL, tau2-Bench, and MCP Universe. These results suggest that general tool use, like other important LLM capabilities, benefits from dedicated mid-training rather than being left entirely to post-training.