ToolGym: an Open-world Tool-using Environment for Scalable Agent Testing and Data Curation

• URL: http://arxiv.org/abs/2601.06328v1
• Date: Fri, 09 Jan 2026 21:59:31 GMT
• Title: ToolGym: an Open-world Tool-using Environment for Scalable Agent Testing and Data Curation
• Abstract summary: We introduce an open-world tool-using environment, built on 5,571 format unified tools across 204 commonly used apps.<n>It includes a task creation engine that synthesizes longhorizon, multi-tool with wild constraints, and a state controller that injects interruptions and failures to stress-test robustness.<n> Comprehensive evaluation of state-of-the-art LLMs reveals the misalignment between tool planning and execution abilities, the constraint following weakness of existing LLMs, and DeepSeek-v3.2's strongest robustness.
• Score: 42.479399507055454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tool-using LLM agents still struggle in open-world settings with large tool pools, long-horizon objectives, wild constraints, and unreliable tool states. For scalable and realistic training and testing, we introduce an open-world tool-using environment, built on 5,571 format unified tools across 204 commonly used apps. It includes a task creation engine that synthesizes long-horizon, multi-tool workflows with wild constraints, and a state controller that injects interruptions and failures to stress-test robustness. On top of this environment, we develop a tool select-then-execute agent framework with a planner-actor decomposition to separate deliberate reasoning and self-correction from step-wise execution. Comprehensive evaluation of state-of-the-art LLMs reveals the misalignment between tool planning and execution abilities, the constraint following weakness of existing LLMs, and DeepSeek-v3.2's strongest robustness. Finally, we collect 1,170 trajectories from our environment to fine-tune LLMs, achieving superior performance to baselines using 119k samples, indicating the environment's value as both a realistic benchmark and a data engine for tool-using agents. Our code and data will be publicly released.

Related papers

• ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning [6.90417084681273]
  ToolVerse is a framework that scales up agentic RL environments and enables agents to perform complex long-horizon reasoning.<n>First, ToolVerse automatically builds the massive executable agent training environments from nearly 400 real-world Model Context Protocols.<n>Third, to alleviate the credit assigment problem in long-horizon agentic RL, we propose a fine-grained Turn-Aware Relative Advantage algorithm.
  arXiv  Detail & Related papers  (2026-07-17T06:12:04Z)
• PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems [59.730861364166174]
  PlanBench-XL is an interactive benchmark of 327 retail tasks over 1,665 tools.<n>It tests whether agents can iteratively retrieve usable tools, invoke them to uncover intermediate evidence for subsequent calls toward the final goal.
  arXiv  Detail & Related papers  (2026-06-21T08:29:12Z)
• SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents [64.59100414726556]
  Large language model (LLM) agents increasingly rely on harnesses that manage context, tools, and multi-turn execution.<n>Retrieval-augmented tool selection offers a natural alternative, but existing one-shot retrieval methods fail to align isolated tool descriptions with the agent's true task intention.<n>We propose SING, an intention-aware active tool discovery framework that builds an intention-tool graph linking user intentions, tool capabilities, and tool collaboration patterns.
  arXiv  Detail & Related papers  (2026-06-15T11:37:37Z)
• ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution [49.30293260493709]
  Large Language Models (LLMs) enhance their problem-solving capability by utilizing external tools.<n>Existing methods relying on static embedding retrieval or parameter memorization struggle to align user intent with tool semantics.<n>We present Tool Omni, a unified agentic framework that enables LLMs for open-world tool use by proactive retrieval and grounded execution.
  arXiv  Detail & Related papers  (2026-04-15T12:26:10Z)
• Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use [21.666294374943178]
  We propose a curriculum learning framework that transfers supervision from trace-rich settings to trace-free deployment.<n> Experiments show consistent gains on unseen tools, strong cross-domain generalization, and robustness as the number of candidate tools scales to over 100.
  arXiv  Detail & Related papers  (2026-02-23T23:50:24Z)
• Gecko: A Simulation Environment with Stateful Feedback for Refining Agent Tool Calls [56.407063247662336]
  We introduce Gecko, a comprehensive environment that simulates tool responses using a combination of rules and LLMs.<n>GATS consistently improves the tool calling performance of various LLMs including GPT-4o, GPT-5, and Gemini-3.0-pro.
  arXiv  Detail & Related papers  (2026-02-22T15:02:00Z)
• ML-Tool-Bench: Tool-Augmented Planning for ML Tasks [23.54937738755734]
  We introduce a benchmark for evaluating tool-augmented machine learning agents.<n>Our benchmark goes beyond traditional tool-use evaluation by incorporating an in-memory named object management.<n>Our approach improves over ReAct by 16.52 percentile positions, taking the median across all Kaggle challenges.
  arXiv  Detail & Related papers  (2025-11-29T23:59:40Z)
• DeepAgent: A General Reasoning Agent with Scalable Toolsets [111.6384541877723]
  DeepAgent is an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution.<n>To address the challenges of long-horizon interactions, we introduce an autonomous memory folding mechanism that compresses past interactions into structured episodic, working, and tool memories.<n>We develop an end-to-end reinforcement learning strategy, namely ToolPO, that leverages LLM-simulated APIs and applies tool-call advantage attribution to assign fine-grained credit to the tool invocation tokens.
  arXiv  Detail & Related papers  (2025-10-24T16:24:01Z)
• MCPVerse: An Expansive, Real-World Benchmark for Agentic Tool Use [72.53177559476704]
  We introduce MCPVerse, a real-world benchmark for evaluating agentic tool use.<n> MCPVerse integrates more than 550 real-world, executable tools to create an unprecedented action space exceeding 140k tokens.<n>We benchmarked the state-of-the-art LLMs across three modes (Oracle, Standard, and Max-Scale)
  arXiv  Detail & Related papers  (2025-08-22T09:47:53Z)
• ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks [64.86209459039313]
  ThinkGeo is an agentic benchmark designed to evaluate tool-augmented agents on remote sensing tasks via structured tool use and multi-step planning.<n>We implement a ReAct-style interaction loop and evaluate both open and closed-source LLMs on 486 structured agentic tasks with 1,773 expert-verified reasoning steps.<n>Our analysis reveals notable disparities in tool accuracy and planning consistency across models.
  arXiv  Detail & Related papers  (2025-05-29T17:59:38Z)
• LLM Agents Making Agent Tools [2.5529148902034637]
  Tool use has turned large language models (LLMs) into powerful agents that can perform complex multi-step tasks.<n>But these tools must be implemented in advance by human developers.<n>We propose ToolMaker, an agentic framework that autonomously transforms papers with code into LLM-compatible tools.
  arXiv  Detail & Related papers  (2025-02-17T11:44:11Z)
• Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios [93.68764280953624]
  UltraTool is a novel benchmark designed to improve and evaluate Large Language Models' ability in tool utilization. It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving. A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage.
  arXiv  Detail & Related papers  (2024-01-30T16:52:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.