Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning

• URL: http://arxiv.org/abs/2505.01441v1
• Date: Mon, 28 Apr 2025 10:42:49 GMT
• Title: Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning
• Authors: Joykirat Singh, Raghav Magazine, Yash Pandya, Akshay Nambi,
• Abstract summary: ARTIST is a unified framework that tightly couples agentic reasoning, reinforcement learning, and tool integration for large language models.<n>It enables models to autonomously decide when, how, and which tools to invoke within multi-turn reasoning chains.<n>Experiments show that ARTIST consistently outperforms state-of-the-art baselines.
• Score: 0.21845291030915975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have achieved remarkable progress in complex reasoning tasks, yet they remain fundamentally limited by their reliance on static internal knowledge and text-only reasoning. Real-world problem solving often demands dynamic, multi-step reasoning, adaptive decision making, and the ability to interact with external tools and environments. In this work, we introduce ARTIST (Agentic Reasoning and Tool Integration in Self-improving Transformers), a unified framework that tightly couples agentic reasoning, reinforcement learning, and tool integration for LLMs. ARTIST enables models to autonomously decide when, how, and which tools to invoke within multi-turn reasoning chains, leveraging outcome-based RL to learn robust strategies for tool use and environment interaction without requiring step-level supervision. Extensive experiments on mathematical reasoning and multi-turn function calling benchmarks show that ARTIST consistently outperforms state-of-the-art baselines, with up to 22% absolute improvement over base models and strong gains on the most challenging tasks. Detailed studies and metric analyses reveal that agentic RL training leads to deeper reasoning, more effective tool use, and higher-quality solutions. Our results establish agentic RL with tool integration as a powerful new frontier for robust, interpretable, and generalizable problem-solving in LLMs.

Related papers

• AutoTIR: Autonomous Tools Integrated Reasoning via Reinforcement Learning [17.086082843274003]
  Large Language Models (LLMs) evolve into powerful Large Reasoning Models (LRMs)<n>Tool-Integrated Reasoning (TIR) further extends their capabilities by incorporating external tools.<n>Inspired by the human ability to adaptively select tools, we introduce AutoTIR, a reinforcement learning framework.
  arXiv  Detail & Related papers  (2025-07-29T14:12:28Z)
• Agentic Reinforced Policy Optimization [66.96989268893932]
  Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
  arXiv  Detail & Related papers  (2025-07-26T07:53:11Z)
• Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning [63.31585771716123]
  Large language models (LLMs) have shown remarkable reasoning capabilities via large-scale reinforcement learning (RL)<n>We introduce Tool-Star, an RL-based framework designed to empower LLMs to autonomously invoke multiple external tools during stepwise reasoning.<n>Tool-Star integrates six types of tools and incorporates systematic designs in both data synthesis and training.
  arXiv  Detail & Related papers  (2025-05-22T09:00:19Z)
• MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering [57.156093929365255]
  Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents.<n>MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios.<n>Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-12T17:35:43Z)
• FamilyTool: A Multi-hop Personalized Tool Use Benchmark [94.1158032740113]
  We introduce FamilyTool, a novel benchmark grounded in a family-based knowledge graph (KG)<n>FamilyTool challenges Large Language Models with queries spanning 1 to 3 relational hops.<n>Experiments reveal significant performance gaps in state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-04-09T10:42:36Z)
• ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [54.787341008881036]
  We introduce Reinforced Meta-thinking Agents (ReMA), a novel framework that leverages Multi-Agent Reinforcement Learning (MARL) to elicit meta-thinking behaviors.<n>ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions.<n> Experimental results demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-12T16:05:31Z)
• Alignment for Efficient Tool Calling of Large Language Models [34.748897353548756]
  Large language models (LLMs) can integrate external tools, enhancing their task performance by expanding their knowledge boundaries.<n>However, relying on tools often introduces tradeoffs between performance, speed, and cost.<n>This paper addresses the challenge of aligning LLMs with their knowledge boundaries to make more intelligent decisions about tool invocation.
  arXiv  Detail & Related papers  (2025-03-09T17:55:49Z)
• Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
  We propose MeCo, an adaptive decision-making strategy for external tool use.<n>MeCo captures high-level cognitive signals in the representation space, guiding when to invoke tools.<n>Our experiments show that MeCo accurately detects LLMs' internal cognitive signals and significantly improves tool-use decision-making.
  arXiv  Detail & Related papers  (2025-02-18T15:45:01Z)
• Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning [12.80689911863731]
  Sibyl is a powerful framework designed to tackle complex reasoning tasks by efficiently leveraging a minimal set of tools. Sibyl implements a multi-agent debate-based jury to self-refine the final answers, ensuring a comprehensive and balanced approach. Our experimental results on the GAIA benchmark test set reveal that the Sibyl agent achieves state-of-the-art performance with an average score of 34.55%.
  arXiv  Detail & Related papers  (2024-07-15T13:45:40Z)
• Efficient Tool Use with Chain-of-Abstraction Reasoning [63.08202389132155]
  Large language models (LLMs) need to ground their reasoning to real-world knowledge.<n>There remains challenges for fine-tuning LLM agents to invoke tools in multi-step reasoning problems.<n>We propose a new method for LLMs to better leverage tools in multi-step reasoning.
  arXiv  Detail & Related papers  (2024-01-30T21:53:30Z)
• TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage [28.554981886052953]
  Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks. This paper proposes a structured framework tailored for LLM-based AI Agents.
  arXiv  Detail & Related papers  (2023-08-07T09:22:03Z)

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.