迴圈語言模型提升組合式工具呼叫

摘要

循環語言模型在推理基準上已展現出令人鼓舞的成果，然而其在智能體工具使用方面的潛力仍 largely 未被探索。我們在組合式工具呼叫的情境中研究此問題；在該情境下，模型必須協調多個 API 呼叫、維護中間狀態，並在工具互動之間保持依賴關係。我們針對 API-Bank、BFCL 與 NESTful 評估原生及改造的循環語言模型，比較在配對監督式微調設定及推論時不同循環深度下訓練的循環與非循環模型。在受控實驗中，循環計算通常有益於組合式及依賴感知的工具使用，而在單獨 API 呼叫上則提供較小且更依賴模型的增益。多步驟工具使用的準確度通常隨循環深度增加而提升；然而，自適應推論透過僅在需要時分配額外計算，達成更有利的計算-效能權衡。我們的結果表明，循環語言模型是適用於需要可靠規劃、協調及執行組合式工具使用工作流程之智能體系統的具有前景的架構。

English

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.