Adaptive Tool Generation with Models as Tools and Reinforcement Learning

• URL: http://arxiv.org/abs/2510.06825v2
• Date: Thu, 09 Oct 2025 04:38:31 GMT
• Title: Adaptive Tool Generation with Models as Tools and Reinforcement Learning
• Authors: Chenpeng Wang, Xiaojie Cheng, Chunye Wang, Linfeng Yang, Lei Zhang,
• Abstract summary: MTR is a simulation-first training framework for tool-augmented reasoning.<n>It learns from complete ReAct traces with schema-validated, simulated observations.<n>MTR attains competitive Exact Match (EM) scores to live-API systems.
• Score: 3.592245101862886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tool-augmented language models have demonstrated strong capabilities, but their reliance on live API access creates scalability and reliability challenges during training and deployment. We propose MTR, a simulation-first training framework for tool-augmented reasoning. Instead of relying on live APIs, MTR learns from complete ReAct traces with schema-validated, simulated observations. Our approach operates through a multi-agent architecture where a ToolMaker generates task-specific, OpenAI-compatible tool interfaces, an AutoAgent produces structured think-act-observe sequences, and a ToolActor simulates realistic responses. Training proceeds in two stages: Stage-1 Supervised Fine-Tuning (SFT) teaches 'trace grammar' from complete reasoning sequences; Stage-2 Group Relative Policy Optimization (GRPO) optimizes strategy with a composite trace reward that balances answer correctness and internal consistency. Across four multi-hop QA benchmarks (HotpotQA, MuSiQue, 2WikiMultiHopQA, Bamboogle), MTR attains competitive Exact Match (EM) scores to live-API systems and excels on reasoning-intensive tasks, suggesting that effective tool reasoning can be learned from structured traces without live interactions.

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