Zhaoyi Zhou, Akshayaa Magesh, Jeongyeol Kwon, Artem Kuriksha, Daniel Jiang, Andrea Zanette, Ayush Jain · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31483 · open-access PDF
由于LLM代理商在超过一万台的工具中运作,为复杂的多步用户指令选择合适的工具成为一个中心的瓶颈。工具描述不能全部融入背景,基于相关性的收集者仅仅在不知道实际需要哪些工具的情况下排名工具。最近的工作取代了克劳德或ChatGPT等专有商业LLM来驱动收集器,分解指令并选择收集的候选人。我们展示了一个小型,开放的模型可以通过增强学习来训练,以执行相同的功能。
Machine translation (Meta M2M100); the English original follows.
As LLM agents operate over corpora of more than ten thousand tools, selecting the right tool for a complex multi-step user instruction becomes a central bottleneck. Tool descriptions cannot all fit in context, and relevance-based retrievers only rank tools without knowing which are actually needed. Recent work instead leverages proprietary commercial LLMs like Claude or ChatGPT to drive a retriever, decomposing the instruction and selecting over retrieved candidates. We show that a small, open model can be trained via reinforcement learning to perform the same functionality. On ToolBench, a Qw
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