ARTIST, a novel framework unifying agentic reasoning, reinforcement learning, and tool integration, enables LLMs to autonomously orchestrate external tools within multi-turn reasoning, achieving up to 22% accuracy gains on complex math tasks and significant improvements in multi-turn function calling over baselines.
Reinforcement Learning, Large Language Model, Agent, Reasoning, Multimodal Systems, Human-AI Interaction
Joykirat Singh, Raghav Magazine, Yash Pandya, Akshay Nambi
Microsoft Research
Generated by grok-3
Background Problem
Large Language Models (LLMs) have made significant strides in complex reasoning tasks, but their reliance on static internal knowledge and text-only reasoning limits their effectiveness in dynamic, real-world problem-solving scenarios that require multi-step reasoning, adaptive decision-making, and interaction with external tools or environments. The key problem addressed by this work is the inability of current LLMs to effectively integrate external resources and adaptively orchestrate tool use during reasoning, often leading to inaccuracies, hallucinations, or failure in knowledge-intensive, time-sensitive, or domain-specific tasks. This paper introduces a framework to overcome these limitations by enabling LLMs to autonomously decide when, how, and which tools to use within multi-turn reasoning chains.
Method
The proposed framework, ARTIST (Agentic Reasoning and Tool Integration in Self-Improving Transformers), unifies agentic reasoning, reinforcement learning (RL), and tool integration for LLMs. Its core idea is to treat tool usage and environment interaction as integral parts of the reasoning process, allowing the model to dynamically interleave text-based thinking with tool queries and outputs in a multi-step reasoning chain. The implementation leverages Group Relative Policy Optimization (GRPO), an RL algorithm that uses outcome-based rewards without requiring intermediate supervision, to train the model on adaptive tool-use strategies. Key steps include: (1) generating reasoning rollouts that alternate between internal reasoning (
Experiment
The experiments evaluate ARTIST in two domains: complex mathematical reasoning and multi-turn function calling, using Qwen2.5-7B and 14B-Instruct models. For math reasoning, training data comprised 20,000 problems from NuminaMath, with evaluation on MATH-500, AIME, AMC, and Olympiad Bench, measuring Pass@1 accuracy. Results showed significant improvements, especially on harder tasks (e.g., up to 22% absolute gain on AMC over base models, outperforming GPT-4o), demonstrating the effectiveness of dynamic tool use (Python interpreter) for complex problems; however, gains were modest on less challenging MATH-500, suggesting limited benefit when internal knowledge suffices. For function calling, training used 100 annotated tasks from BFCL v3, with evaluation on τ-bench (Airline, Retail) and BFCL v3 subsets (Missing Function, Parameters, Long Context). ARTIST doubled accuracy on τ-bench over base models (e.g., 0.260 vs. 0.120 on Airline) but showed smaller gains on some BFCL v3 subsets (e.g., +0.5% on Missing Parameters), indicating inconsistent performance across task types. The setup was comprehensive, comparing against frontier LLMs (GPT-4o), open-source tool-augmented models (ToRA), and prompt-based baselines, with metrics like reward score, tool calls, and reasoning length highlighting deeper reasoning and efficient tool use. However, the lack of discussion on failure modes (e.g., tool errors) and potential overfitting to specific benchmarks raises concerns about real-world robustness. Overall, while results align with expectations for complex tasks, the variability in gains suggests the method’s impact depends heavily on task complexity and domain.
Further Thoughts
The ARTIST framework opens intriguing avenues for future exploration, particularly in how agentic reasoning could intersect with other AI domains like robotics or real-time decision systems, where dynamic interaction with physical or digital environments is paramount. One insightful connection is to Retrieval-Augmented Generation (RAG) systems, where ARTIST’s adaptive tool-use strategies could enhance real-time information retrieval by learning to prioritize and sequence search queries based on context, potentially reducing latency and improving relevance over static RAG approaches. However, a critical concern is the framework’s robustness in adversarial or noisy environments—current experiments assume reliable tool outputs, but real-world tools (e.g., APIs with rate limits or web searches with irrelevant results) often fail or mislead. Extending ARTIST to incorporate error-handling mechanisms or uncertainty quantification, perhaps by integrating probabilistic reasoning from Bayesian methods, could address this gap. Additionally, the emergent self-correction and self-reflection behaviors suggest potential alignment with human-in-the-loop systems, where ARTIST could iteratively refine solutions based on human feedback, enhancing trust and interpretability in critical applications like healthcare or finance. These directions highlight the need to test ARTIST beyond controlled benchmarks, exploring its scalability and safety in open-ended, high-stakes scenarios.