mlsanityData to 14 Oct 2026

ToolBrain: A Flexible Reinforcement Learning Framework for Agentic Tools

arXiv 2510.00023 ↗First posted 24 Sep 2025cs.AIcited by 2

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

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 training tool use in agentic models with flexible reinforcement learning, thereby easing the barriers for researchers and practitioners to adapt LLM-based agents to specific domains. It supports a wide range of training strategies, including reinforcement learning 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, 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 an Email Search Agent case study, showing measurable improvements in tool-use skills under a realistic workflow, while keeping the codebase simple and extensible. Our framework is publicly available at https://toolbrain.org/.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 26 of its 35 references that are in the map. First posted on arXiv 24 Sep 2025.

Builds on

26 of 35 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Agent Lightning: Train ANY AI Agents with Reinforcement Learning5 Aug
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learning20 Mar

Other references · 24

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  6. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  7. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  8. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  9. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  10. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  11. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  12. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
12 more
  1. 2017Deep reinforcement learning from human preferencescited by 3,912
  2. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  3. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  4. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  5. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  6. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  7. 2024A Survey on LLM-as-a-Judgecited by 1,423
  8. 2017Mixed Precision Trainingcited by 1,156
  9. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
  10. 2022Large Language Models Can Self-Improvecited by 664
  11. 2021Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Compositioncited by 30
  12. 2022Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agentscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learningcited by 31
  2. 2025RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learningcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026AgentTune: A Unified Training Lifecycle for Tool-Using LLM Agents
  2. 2026Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning
  3. 2026ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning
  4. 2025ToolRM: Towards Agentic Tool-Use Reward Modeling
  5. 2026CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use
  6. 2025Tool-R1: Sample-Efficient Reinforcement Learning for Agentic Tool Use
  7. 2025AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning
  8. 2026Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning