mlsanityData to 14 Oct 2026

Learning Evolving Tools for Large Language Models

arXiv 2410.06617 ↗First posted 9 Oct 2024cs.CL cs.AIcited by 16

On the map: LLM agents and tool use → Tool learning and function calling

Abstract (arXiv)

Tool learning enables large language models (LLMs) to interact with external tools and APIs, greatly expanding the application scope of LLMs. However, due to the dynamic nature of external environments, these tools and APIs may become outdated over time, preventing LLMs from correctly invoking tools. Existing research primarily focuses on static environments and overlooks this issue, limiting the adaptability of LLMs in real-world applications. In this paper, we propose ToolEVO, a novel framework designed to enhance the adaptive and reflective capabilities of LLMs against tool variability. By leveraging Monte Carlo Tree Search, ToolEVO facilitates active exploration and interaction of LLMs within dynamic environments, allowing for autonomous self-reflection and self-updating of tool usage based on environmental feedback. Additionally, we introduce ToolQA-D, a benchmark specifically designed to evaluate the impact of tool variability. Extensive experiments demonstrate the effectiveness and stability of our approach, highlighting the importance of adaptability to tool variability for effective tool learning. Code: https://github.com/Chen-GX/ToolEVO

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 39 of its 60 references that are in the map. First posted on arXiv 9 Oct 2024.

Builds on

39 of 60 references.

Closest ancestors · 15

Same direction, newest first

  1. 2024Can Tool-augmented Large Language Models be Aware of Incomplete Conditions?18 Jun
  2. 2024Tool Learning with Large Language Models: A Survey28 May
  3. 2024StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models12 Mar
  4. 2024LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error7 Mar
  5. 2024AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls6 Feb
  6. 2023MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use4 Oct
  7. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul
  8. 2023ToolQA: A Dataset for LLM Question Answering with External Tools23 Jun
  9. 2023ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases8 Jun
  10. 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction30 May
  11. 2023Making Language Models Better Tool Learners with Execution Feedback22 May
  12. 2023Tool Learning with Foundation Models17 Apr
3 more
  1. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs14 Apr
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb
  3. 2022TALM: Tool Augmented Language Models24 May

Other references · 24

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  5. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  6. 2023FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioningcited by 2,598
  7. 2024Qwen2 Technical Reportcited by 2,120
  8. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
  9. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  10. 2023ChatDev: Communicative Agents for Software Developmentcited by 919
  11. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 634
  12. 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviorscited by 598
12 more
  1. 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 295
  2. 2021ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learningcited by 256
  3. 2024Trial and Error: Exploration-Based Trajectory Optimization for LLM Agentscited by 197
  4. 2023TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIscited by 184
  5. 2023Planning with Large Language Models for Code Generationcited by 182
  6. 2024AlphaMath Almost Zero: Process Supervision without Processcited by 162
  7. 2023ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Searchcited by 105
  8. 2024Small LLMs Are Weak Tool Learners: A Multi-LLM Agentcited by 93
  9. 2022Solving Math Word Problems via Cooperative Reasoning induced Language Modelscited by 77
  10. 2024Step-level Value Preference Optimization for Mathematical Reasoningcited by 72
  11. 2024Tools Fail: Detecting Silent Errors in Faulty Toolscited by 25
  12. 2023MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Buildercited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 11.

16 papers.

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By area:

  1. 2025Lifelong Learning of Large Language Model based Agents: A Roadmapcited by 83
  2. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directionscited by 50
  3. 2024From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactionscited by 46
  4. 2026PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystemscited by 11
  5. 2025C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generationcited by 9
  6. 2026PhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planningcited by 9
  7. 2026The World Won't Stay Still: Programmable Evolution for Agent Benchmarkscited by 5
  8. 2025NaviAgent: Graph-Driven Bilevel Planning for Scalable Tool Orchestrationcited by 3
  9. 2026A Component-Based Survey of Interactions between Large Language Models and Multi-Armed Banditscited by 1
  10. 2026Gecko: A Simulation Environment with Stateful Feedback for Refining Agent Tool Callscited by 1
  11. 2026Agents in the Large: Perception-Centered Architecture for Persistent Agentscited by 1
  12. 2026Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidancecited by 0
4 more
  1. 2026Compositional Safety Failures in Harness Evolution: Identification and Runtime Monitoringcited by 0
  2. 2026Applying Language Models in Clinical Medicine: Recent Trends and Perspectivescited by 0
  3. 2026Towards Reliable AI Data Scientists: Data Agents with Workflow Harnessescited by 0
  4. 2026One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustnesscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution
  2. 2025Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments
  3. 2025ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning
  4. 2024What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
  5. 2026Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction
  6. 2025Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learning
  7. 2024Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
  8. 2024ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios