Looped Language Models Improve Compositional Tool Calling

Abstract:Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We study this question in compositional tool-calling settings, where models must coordinate multiple API calls, maintain intermediate state, and preserve dependencies across tool interactions. We evaluate native and retrofitted looped language models on API-Bank, BFCL, and NESTful, comparing looped and non-looped models trained under matched supervised fine-tuning recipes and varying recurrent depth at inference time. In controlled experiments, recurrent computation generally benefits compositional and dependency-aware tool use, while providing smaller and more model-dependent gains on isolated API invocation. Accuracy on multi-step tool use generally increases with recurrent depth; adaptive inference, however, achieves a more favorable compute-performance trade-off by allocating additional computation only when needed. Our results suggest that looped language models are a promising architecture for agentic systems that require reliable planning, coordination, and execution of compositional tool use workflows.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2608.18171 [cs.AI]

(or arXiv:2608.18171v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.18171 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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