# Planning and Editing What You Retrieve for Enhanced Tool Learning

arXiv: https://arxiv.org/abs/2404.00450 · HTML: https://mlsanity.com/paper/2404.00450/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)

## Builds on (most cited first, 25 of 25)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Visual Programming: Compositional visual reasoning without training](https://mlsanity.com/paper/2211.11559/) (2022)
- [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023)
- [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://mlsanity.com/paper/2310.01558/) (2023)
- [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://mlsanity.com/paper/2209.14610/) (2022)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://mlsanity.com/paper/2305.11554/) (2023)
- [GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information](https://mlsanity.com/paper/2304.09667/) (2023)
- [Large Language Models for Automated Open-domain Scientific Hypotheses Discovery](https://mlsanity.com/paper/2309.02726/) (2023)
- [Exploring Continual Learning for Code Generation Models](https://mlsanity.com/paper/2307.02435/) (2023)
- [Don't Fine-Tune, Decode: Syntax Error-Free Tool Use via Constrained Decoding](https://mlsanity.com/paper/2310.07075/) (2023)
- [Dialog2API: Task-Oriented Dialogue with API Description and Example Programs](https://mlsanity.com/paper/2212.09946/) (2022)

## Built on by (most cited first, 21 of 21)

- [Tool Learning with Large Language Models: A Survey](https://mlsanity.com/paper/2405.17935/) (2024)
- [Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases](https://mlsanity.com/paper/2410.14594/) (2024)
- [Guided Profile Generation Improves Personalization with LLMs](https://mlsanity.com/paper/2409.13093/) (2024)
- [Enhancing Tool Retrieval with Iterative Feedback from Large Language Models](https://mlsanity.com/paper/2406.17465/) (2024)
- [Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems](https://mlsanity.com/paper/2511.01854/) (2025)
- [Teaching Language Models To Gather Information Proactively](https://mlsanity.com/paper/2507.21389/) (2025)
- [GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation](https://mlsanity.com/paper/2502.18990/) (2025)
- [Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systems](https://mlsanity.com/paper/2511.16654/) (2025)
- [FoodPuzzle: Developing Large Language Model Agents as Flavor Scientists](https://mlsanity.com/paper/2409.12832/) (2024)
- [GraphicBench: A Planning Benchmark for Graphic Design with Language Agents](https://mlsanity.com/paper/2504.11571/) (2025)
- [VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction](https://mlsanity.com/paper/2509.06736/) (2025)
- [MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation](https://mlsanity.com/paper/2510.18383/) (2025)
- [Multi-Field Tool Retrieval](https://mlsanity.com/paper/2602.05366/) (2026)
- [Tool Retrieval Bridge: Aligning Vague Instructions with Retriever Preferences via Bridge Model](https://mlsanity.com/paper/2604.07816/) (2026)
- [Enhancing Tool Calling in LLMs with the International Tool Calling Dataset](https://mlsanity.com/paper/2603.05515/) (2026)
- [MagicSelector: Joint Optimization for Agent Tool Selection via Counterfactual Decomposition and Progressive Reranking](https://mlsanity.com/paper/2607.17751/) (2026)
- [TUMS: Enhancing Tool-use Abilities of LLMs with Multi-structure Handlers](https://mlsanity.com/paper/2505.08402/) (2025)
- [Enrich-Retrieve-Rank: Scaling Capability Discovery Beyond In-Context Routing](https://mlsanity.com/paper/2608.22695/) (2026)
- [Toollery: Scaling LLM Agents to Thousands of Skills and Tools](https://mlsanity.com/paper/2609.22218/) (2026)
- [Toward Interactive Understanding of Code APIs](https://mlsanity.com/paper/2609.32081/) (2026)
- [Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning](https://mlsanity.com/paper/2609.36178/) (2026)

## Abstract (arXiv)

Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and smart assistants. However, existing methods, relying on simple one-time retrieval strategies, fall short on effectively and accurately shortlisting relevant tools. This paper introduces a novel PLUTO (Planning, Learning, and Understanding for TOols) approach, encompassing `Plan-and-Retrieve (P&R)` and `Edit-and-Ground (E&G)` paradigms. The P&R paradigm consists of a neural retrieval module for shortlisting relevant tools and an LLM-based query planner that decomposes complex queries into actionable tasks, enhancing the effectiveness of tool utilization. The E&G paradigm utilizes LLMs to enrich tool descriptions based on user scenarios, bridging the gap between user queries and tool functionalities. Experiment results demonstrate that these paradigms significantly improve the recall and NDCG in tool retrieval tasks, significantly surpassing current state-of-the-art models.