ToolBrain: A Flexible Reinforcement Learning Framework for Agentic Tools

• URL: http://arxiv.org/abs/2510.00023v1
• Date: Wed, 24 Sep 2025 16:01:05 GMT
• Title: ToolBrain: A Flexible Reinforcement Learning Framework for Agentic Tools
• Authors: Quy Minh Le, Minh Sao Khue Luu, Khanh-Tung Tran, Duc-Hai Nguyen, Hoang-Quoc-Viet Pham, Quan Le, Hoang Thanh Lam, Hoang D. Nguyen,
• Abstract summary: ToolBrain is a framework for coaching tool use in agentic models with flexible reinforcement learning (RL)<n>It supports a wide range of training strategies, including RL algorithms such as GRPO and DPO, as well as supervised learning.<n>It is packed with useful capabilities, including knowledge distillation from large to small models for efficient development, automatic task generation from tool descriptions, seamless tool retrieval, efficient fine-tuning pipelines with QLoRA through Unsloth, and quantized inference via bitsandbytes.
• Score: 4.751741320941162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective tool use is essential for agentic AI, yet training agents to utilize tools remains challenging due to manually designed rewards, limited training data, and poor multi-tool selection, resulting in slow adaptation, wasted computational resources, and suboptimal performance. We introduce ToolBrain, a lightweight and user-friendly framework for coaching tool use in agentic models with flexible reinforcement learning (RL), easing the barriers for researchers and practitioners to adapt LLM-based agents to specific domains. It supports a wide range of training strategies, including RL algorithms such as GRPO and DPO, as well as supervised learning. ToolBrain enables custom reward callables directly on an agent's execution traces or simply utilizes an automated LLM-as-a-judge system for reward generation. It is packed with useful capabilities, including knowledge distillation from large to small models for efficient development, automatic task generation from tool descriptions, seamless tool retrieval, efficient fine-tuning pipelines with QLoRA through Unsloth, and quantized inference via bitsandbytes. We demonstrate ToolBrain through diverse use cases, such as training a CodeAct agent to autonomously execute email search tasks, showing fast, targeted improvements (up to 30.0%) in tool-use skills while keeping the codebase simple and extensible in Agentic AI. Our framework is publicly available at https://toolbrain.org.

Related papers

• Evolving from Tool User to Creator via Training-Free Experience Reuse in Multimodal Reasoning [16.12114923351562]
  We propose a training-free framework that transforms agents from tool users to tool creators.<n>This approach harvests reasoning experiences and distills them into reusable assets.<n>We also introduce a memory consolidation mechanism to maintain the tool library.
  arXiv  Detail & Related papers  (2026-02-02T11:37:45Z)
• AutoTool: Efficient Tool Selection for Large Language Model Agents [10.061664247482488]
  Large Language Model (LLM) agents have emerged as powerful tools for automating complex tasks by leveraging the reasoning and decision-making abilities of LLMs.<n>However, a major bottleneck lies in the high inference cost of tool selection, especially in approaches like ReAct that repeatedly invoke the LLM to determine which tool to use at each step.<n>We propose AutoTool, a novel graph-based framework that bypasses repeated LLM inference by exploiting a key empirical observation: tool usage inertia.
  arXiv  Detail & Related papers  (2025-11-18T16:41:48Z)
• Multi-Agent Tool-Integrated Policy Optimization [67.12841355267678]
  Large language models (LLMs) increasingly rely on multi-turn tool-integrated planning for knowledge-intensive and complex reasoning tasks.<n>Existing implementations typically rely on a single agent, but they suffer from limited context length and noisy tool responses.<n>No existing methods support effective reinforcement learning post-training of tool-integrated multi-agent frameworks.
  arXiv  Detail & Related papers  (2025-10-06T10:44:04Z)
• Tool-R1: Sample-Efficient Reinforcement Learning for Agentic Tool Use [50.02614257515131]
  Large language models (LLMs) have demonstrated strong capabilities in language understanding and reasoning.<n>We propose Tool-R1, a reinforcement learning framework that enables LLMs to perform general, compositional, and multi-step tool use.
  arXiv  Detail & Related papers  (2025-09-16T09:22:21Z)
• Advancing SLM Tool-Use Capability using Reinforcement Learning [0.0]
  The ability to use tools effectively has become a defining feature of Large Language Models (LLMs), allowing them to access external data and internal resources.<n>Small Language Models (SLMs) face challenges in accurately integrating tool use, especially in resource-constrained settings.<n>This study investigates how Reinforcement Learning, specifically Group Relative Policy Optimization, can enhance the tool-use of SLMs.
  arXiv  Detail & Related papers  (2025-09-03T07:41:14Z)
• VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use [78.29315418819074]
  We introduce VerlTool, a unified and modular framework that addresses limitations through systematic design principles.<n>Our framework formalizes ARLT as multi-turn trajectories with multi-modal observation tokens (text/image/video), extending beyond single-turn RLVR paradigms.<n>The modular plugin architecture enables rapid tool integration requiring only lightweight Python definitions.
  arXiv  Detail & Related papers  (2025-09-01T01:45:18Z)
• AgentFly: Extensible and Scalable Reinforcement Learning for LM Agents [25.735754822676277]
  Language model (LM) agents have gained significant attention for their ability to autonomously complete tasks.<n> reinforcement learning (RL) has been explored to enhance LM's capabilities, such as reasoning and factuality.<n>We built AgentFly, a scalable and Agent-RL framework designed to empower LM agents with a variety of RL algorithms.
  arXiv  Detail & Related papers  (2025-07-20T10:22:36Z)
• ToolRL: Reward is All Tool Learning Needs [54.16305891389931]
  Large Language Models (LLMs) often undergo supervised fine-tuning (SFT) to acquire tool use capabilities.<n>Recent advancements in reinforcement learning (RL) have demonstrated promising reasoning and generalization abilities.<n>We present the first comprehensive study on reward design for tool selection and application tasks within the RL paradigm.
  arXiv  Detail & Related papers  (2025-04-16T21:45:32Z)
• ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning [84.69651852838794]
  Tool learning allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks.<n>We propose ToolACE-R, a novel framework that includes both model-aware iterative training and adaptive refinement for tool learning.<n>We conduct extensive experiments across several benchmark datasets, showing that ToolACE-R achieves competitive performance compared to advanced API-based models.
  arXiv  Detail & Related papers  (2025-04-02T06:38:56Z)
• ToolGen: Unified Tool Retrieval and Calling via Generation [34.34787641393914]
  We introduce ToolGen, a paradigm shift that integrates tool knowledge directly into the large language models' parameters.<n>We show that ToolGen achieves superior results in both tool retrieval and autonomous task completion.<n>ToolGen paves the way for more versatile, efficient, and autonomous AI systems.
  arXiv  Detail & Related papers  (2024-10-04T13:52:32Z)
• Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
  Augmenting large language models with external tools has emerged as a promising approach to extend their utility.<n>Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning.<n>We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)

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.