title: Agentic Reasoning and Tool Integration for LLMs via Reinforcement
    Learning
summary: 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.
paper_authors._id: 68198aea57d4de18fb3e69d6
paper_authors.name: Joykirat Singh
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paper_authors.fullname: Singh
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paper_authors.user: joykirat
paper_authors._id: 68198aea57d4de18fb3e69d7
paper_authors.name: Raghav Magazine
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paper_authors.fullname: Raghav Magazine
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paper_authors.user: Raghav2002
paper_authors._id: 68198aea57d4de18fb3e69d8
paper_authors.name: Yash Pandya
paper_authors.status: extracted_confirmed
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paper_authors._id: 64aba383fddf117e6e5ba818
paper_authors.avatarUrl: /avatars/ee7d25d865b34be5902872d060ad9153.svg
paper_authors.fullname: Akshay  Nambi
paper_authors.type: user
paper_authors.user: akshaynambi
paper_authors._id: 68198aea57d4de18fb3e69d9
paper_authors.name: Akshay Nambi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:06:46.024Z
paper_authors._id: 64aba383fddf117e6e5ba818
paper_authors.avatarUrl: /avatars/ee7d25d865b34be5902872d060ad9153.svg
paper_authors.fullname: Akshay  Nambi
paper_authors.type: user
paper_authors.user: akshaynambi
paper_title: Agentic Reasoning and Tool Integration for LLMs via Reinforcement
    Learning
paper_summary: 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.
paper_ai_summary: ARTIST integrates agentic reasoning and reinforcement learning to enhance LLMs' ability to dynamically use tools and interact with environments, significantly improving performance on complex reasoning tasks.
paper_ai_keywords: agentic reasoning reinforcement learning tool integration multi-turn reasoning RL outcome-based RL mathematical reasoning multi-turn function calling deep reasoning effective tool use generalizable problem-solving