Beyond Single-Shot: Multi-step Tool Retrieval via Query Planning
Wei Fang, James Glass

TL;DR
TOOLQP introduces an iterative query planning framework for tool retrieval in LLM agents, significantly improving accuracy and generalization over traditional single-shot methods by decomposing complex instructions into sub-tasks.
Contribution
The paper presents TOOLQP, a novel approach that models retrieval as iterative query planning, enabling better handling of complex requests and tool compositions in LLM agents.
Findings
Achieves state-of-the-art performance in tool retrieval tasks.
Demonstrates superior zero-shot generalization capabilities.
Shows robustness across diverse retriever architectures.
Abstract
LLM agents operating over massive, dynamic tool libraries rely on effective retrieval, yet standard single-shot dense retrievers struggle with complex requests. These failures primarily stem from the disconnect between abstract user goals and technical documentation, and the limited capacity of fixed-size embeddings to model combinatorial tool compositions. To address these challenges, we propose TOOLQP, a lightweight framework that models retrieval as iterative query planning. Instead of single-shot matching, TOOLQP decomposes instructions into sub-tasks and dynamically generates queries to interact with the retriever, effectively bridging the semantic gap by targeting the specific sub-tasks required for composition. We train TOOLQP using synthetic query trajectories followed by optimization via Reinforcement Learning with Verifiable Rewards (RLVR). Experiments demonstrate that TOOLQP…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Topic Modeling · Multimodal Machine Learning Applications
