Agentic Reasoning and Tool Integration for LLMs via Reinforcement
  Learning
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.
68198aea57d4de18fb3e69d6
Joykirat Singh
claimed_verified
2025-05-06T08:33:08.255Z
61ffaa2943eb0913fa2df74a
/avatars/a19971f830abb8a8ae95e5800beb9fcd.svg
Singh
user
joykirat
68198aea57d4de18fb3e69d7
Raghav Magazine
admin_assigned
2025-05-06T09:06:32.498Z
622ca32345261ac5cc0bdade
/avatars/7e1d633be69cf86a3affb9168b1cc27b.svg
Raghav Magazine
user
Raghav2002
68198aea57d4de18fb3e69d8
Yash Pandya
extracted_confirmed
2025-05-06T04:08:10.843Z
64aba383fddf117e6e5ba818
/avatars/ee7d25d865b34be5902872d060ad9153.svg
Akshay  Nambi
user
akshaynambi
68198aea57d4de18fb3e69d9
Akshay Nambi
admin_assigned
2025-05-06T09:06:46.024Z
64aba383fddf117e6e5ba818
/avatars/ee7d25d865b34be5902872d060ad9153.svg
Akshay  Nambi
user
akshaynambi
Agentic Reasoning and Tool Integration for LLMs via Reinforcement
  Learning
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.
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.
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