ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution

• URL: http://arxiv.org/abs/2505.07512v1
• Date: Mon, 12 May 2025 12:48:30 GMT
• Title: ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution
• Authors: Xu Huang, Weiwen Liu, Xingshan Zeng, Yuefeng Huang, Xinlong Hao, Yuxian Wang, Yirong Zeng, Chuhan Wu, Yasheng Wang, Ruiming Tang, Defu Lian,
• Abstract summary: We propose ToolACE-DEV, a self-improving framework for tool learning.<n>First, we decompose the tool-learning objective into sub-tasks that enhance basic tool-making and tool-using abilities.<n>We then introduce a self-evolving paradigm that allows lightweight models to self-improve, reducing reliance on advanced LLMs.
• Score: 77.86222359025011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The tool-using capability of large language models (LLMs) enables them to access up-to-date external information and handle complex tasks. Current approaches to enhancing this capability primarily rely on distilling advanced models by data synthesis. However, this method incurs significant costs associated with advanced model usage and often results in data compatibility issues, led by the high discrepancy in the knowledge scope between the advanced model and the target model. To address these challenges, we propose ToolACE-DEV, a self-improving framework for tool learning. First, we decompose the tool-learning objective into sub-tasks that enhance basic tool-making and tool-using abilities. Then, we introduce a self-evolving paradigm that allows lightweight models to self-improve, reducing reliance on advanced LLMs. Extensive experiments validate the effectiveness of our approach across models of varying scales and architectures.

Related papers

• ToolACE-R: Tool Learning with Adaptive Self-Refinement [84.69651852838794]
  Tool learning allows Large Language Models to leverage external tools for solving complex user tasks.<n>We propose ToolACE-R, a novel method that introduces adaptive self-refinement for tool invocations.<n>Our results demonstrate the effectiveness of the proposed method, which is compatible with base models of various sizes.
  arXiv  Detail & Related papers  (2025-04-02T06:38:56Z)
• Improving Vision-Language-Action Model with Online Reinforcement Learning [17.043068379668842]
  Recent studies have successfully integrated large vision-language models into low-level robotic control by supervised fine-tuning.<n>We propose iRe-VLA framework, which iterates between Reinforcement Learning and Supervised Learning to effectively improve VLA models.
  arXiv  Detail & Related papers  (2025-01-28T02:53:48Z)
• iTool: Boosting Tool Use of Large Language Models via Iterative Reinforced Fine-Tuning [39.65877861652369]
  Augmenting large language models with external tools is a promising approach to enhancing their capabilities.<n>We show that training gains significantly decay as synthetic data increases.<n>We propose an iterative reinforced fine-tuning strategy designed to alleviate these challenges.
  arXiv  Detail & Related papers  (2025-01-15T04:52:34Z)
• From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions [60.733557487886635]
  This paper focuses on bridging the comprehension gap between Large Language Models and external tools.<n>We propose a novel framework, DRAFT, aimed at Dynamically Refining tool documentation.<n>This methodology pivots on an innovative trial-and-error approach, consisting of three distinct learning phases.
  arXiv  Detail & Related papers  (2024-10-10T17:58:44Z)
• Learning Evolving Tools for Large Language Models [44.25796648300785]
  Tool learning enables large language models (LLMs) to interact with external tools and APIs.<n>Existing research primarily focuses on static environments and overlooks this issue.<n>We propose ToolEVO, a novel framework designed to enhance the adaptive and reflective capabilities of LLMs against tool variability.
  arXiv  Detail & Related papers  (2024-10-09T07:14:45Z)
• LLM With Tools: A Survey [0.0]
  This paper delves into the methodology,challenges, and developments in the realm of teaching LLMs to use external tools. We introduce a standardized paradigm for tool integration guided by a series of functions that map user instructions to actionable plans. Our exploration reveals the various challenges encountered, such as tool invocation timing, selection accuracy, and the need for robust reasoning processes.
  arXiv  Detail & Related papers  (2024-09-24T14:08:11Z)
• CITI: Enhancing Tool Utilizing Ability in Large Language Models without Sacrificing General Performance [17.723293304671877]
  We propose a Component-based Tool-utilizing ability Injection method (CITI) According to the gradient-based importance score of different components, CITI alleviates the capability conflicts caused by fine-tuning process. Experimental results demonstrate that our approach achieves outstanding performance across a range of evaluation metrics.
  arXiv  Detail & Related papers  (2024-09-20T04:06:28Z)
• Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes [72.75421975804132]
  Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting. We propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem. Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives.
  arXiv  Detail & Related papers  (2023-09-11T14:16:37Z)
• Tool Learning with Foundation Models [158.8640687353623]
  With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field.
  arXiv  Detail & Related papers  (2023-04-17T15:16:10Z)
• Scaling Vision-Language Models with Sparse Mixture of Experts [128.0882767889029]
  We show that mixture-of-experts (MoE) techniques can achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost. Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling vision-language models.
  arXiv  Detail & Related papers  (2023-03-13T16:00:31Z)

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.