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arXiv · 2506.11045

Procedural Environment Generation for Tool-Use Agents

Michael Sullivan·Mareike Hartmann·Alexander Koller
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Abstract

Although the power of LLM tool-use agents has ignited a flurry of recent research in this area, the curation of tool-use training data remains an open problem$-$especially for online RL training. Existing approaches to synthetic tool-use data generation tend to be non-interactive, and/or non-compositional. We introduce RandomWorld, a pipeline for the procedural generation of interactive tools and compositional tool-use data. We show that models tuned via SFT and RL on synthetic RandomWorld data improve on a range of tool-use benchmarks, and set the new SoTA for two metrics on the NESTFUL dataset. Further experiments show that downstream performance scales with the amount of RandomWorld-generated training data, opening up the possibility of further improvement through the use of entirely synthetic data.

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BibTeXRIS

Michael Sullivan, Mareike Hartmann, Alexander Koller. 2025-05-21. Procedural Environment Generation for Tool-Use Agents. https://arxiv.org/abs/2506.11045

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