Summary
MidTool is an open corpus-construction pipeline for mid-training large language models on general-purpose agentic tool use, introduced in an arXiv paper (2608.20314) by Fengqing Jiang, Yite Wang, Boyi Liu, and Zhaoyang Wang. The pipeline combines large-scale web, PDF, and code data with synthetic supervision derived from real-world tool APIs, MCP skills, and documentation-anchored workflows. It is designed to teach models to recognize tool functionality, ground arguments in context, compose multi-step tool-calling workflows, and recover from incomplete information. The authors apply mid-training on Qwen3-4B-Base and Qwen3-8B-Base, followed by supervised fine-tuning (SFT) and reinforcement learning. Compared with baselines, the resulting MidTool-Mix consistently improves downstream performance on BFCL, tau2-Bench, and MCP Universe. The findings suggest that general tool use, like other key LLM capabilities, benefits from dedicated mid-training rather than being left entirely to post-training.
Paper Overview
- Research area: Machine Learning
- Authors: Fengqing Jiang, Yite Wang, Boyi Liu, Zhaoyang Wang
- Published: 2026-08-22
- arXiv: 2608.20314
Summary
Mid-training is increasingly regarded as a critical stage for shaping large language model capabilities. Recent work has shown that targeted mid-training can enhance reasoning-intensive abilities and improve agentic capabilities in software engineering environments. This paper investigates a parallel but less-explored agentic capability: general-purpose tool use.
The authors propose MidTool, an open corpus-construction pipeline for mid-training on agentic tool use. It combines large-scale web, PDF, and code data with synthetic supervision derived from real-world tool APIs, MCP skills, and documentation-anchored workflows.
MidTool is designed to teach models to:
- Recognize tool functionality
- Ground arguments in context
- Compose tool-calling workflows
- Recover from incomplete information
The authors perform mid-training on
Qwen3-4B-Base and
Qwen3-8B-Base, followed by SFT and RL post-training. Compared with baselines,
MidTool-Mix consistently improves downstream performance on
BFCL,
tau2-Bench, and
MCP Universe.
Conclusion
The results indicate 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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*Auto-collected on 2026-08-22*
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