Paper Detail

MidTool: Mid-training Data Synthesis for Agentic Tool Use

Fengqing Jiang, Yite Wang, Boyi Liu, Zhaoyang Wang, Canwen Xu, Zhewei Yao, Radha Poovendran, Yuxiong He

arxiv Score 18.8

Published 2026-08-20 · First seen 2026-08-21

General AI

Abstract

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.

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BibTeX

@article{jiang2026midtool,
  title = {MidTool: Mid-training Data Synthesis for Agentic Tool Use},
  author = {Fengqing Jiang and Yite Wang and Boyi Liu and Zhaoyang Wang and Canwen Xu and Zhewei Yao and Radha Poovendran and Yuxiong He},
  year = {2026},
  abstract = {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 c},
  url = {https://arxiv.org/abs/2608.20314},
  keywords = {cs.AI},
  eprint = {2608.20314},
  archiveprefix = {arXiv},
}

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