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MidTool: Mid-training Data Synthesis for Agentic Tool Use

Research LLM Agents

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Merged summary

TL;DR - MidTool is a data-construction pipeline and open corpus for teaching LLMs general tool use during mid-training. Experiments on Qwen3 base models suggest dedicated mid-training improves agentic tool-use performance beyond relying solely on supervised or reinforcement-learning post-training.

  • Synthesizes supervision from real-world APIs, MCP skills, and document-grounded workflows using web, PDF, and code data.
  • Targets tool-affordance recognition, context-grounded arguments, multi-step tool-call composition, and recovery from incomplete information.
  • MidTool-Mix was used to mid-train Qwen3-4B-Base and Qwen3-8B-Base before follow-up supervised fine-tuning and reinforcement learning.
  • It consistently improved downstream results over baselines under both post-training methods on BFCL, tau2-Bench, and MCP Universe.

Sources (1)

MidTool: Mid-training Data Synthesis for Agentic Tool Use

arXiv cs.AI Fengqing Jiang, Yite Wang, Boyi Liu, Zhaoyang Wang, Canwen Xu, Zhewei Yao, Radha Poovendran, Yuxiong He 2026-08-20 arXiv:2608.20314
Public signals Hugging Face upvotes 0
Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-22 14:33:19.456050 UTC

TL;DR - MidTool is a data-construction pipeline and open corpus for teaching LLMs general tool use during mid-training. Experiments on Qwen3 base models suggest dedicated mid-training improves agentic tool-use performance beyond relying solely on supervised or reinforcement-learning post-training.

  • Synthesizes supervision from real-world APIs, MCP skills, and document-grounded workflows using web, PDF, and code data.
  • Targets tool-affordance recognition, context-grounded arguments, multi-step tool-call composition, and recovery from incomplete information.
  • MidTool-Mix was used to mid-train Qwen3-4B-Base and Qwen3-8B-Base before follow-up supervised fine-tuning and reinforcement learning.
  • It consistently improved downstream results over baselines under both post-training methods on BFCL, tau2-Bench, and MCP Universe.
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