MidTool: Mid-training Data Synthesis for Agentic Tool Use

Mid-training 对推理密集任务（数学/科学）有效已被证明，但通用工具使用能力的 mid-training 研究很少

MidTool：为通用工具使用合成中期训练数据，覆盖多类工具调用模式，针对性强化 mid-training

在多个工具调用基准上大幅提升，超过仅靠 fine-tuning 的基线；mid-training 对工具使用同样有效

提升 agent 工具调用基础能力的训练方法，是构建可靠工具增强 agent 的关键步骤

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.