Agentic RL with Tool Use (ARLT)

• ARLT is a paradigm that couples reinforcement learning with autonomous tool discovery and usage to extend agents' reasoning and task-solving abilities.
• It leverages hierarchical architectures and multi-stage RL pipelines to decouple high-level planning from low-level tool control, enhancing adaptability.
• Empirical studies show improved generalization and sample efficiency through structured reward mechanisms and dynamic, multi-modal tool integration.

Agentic Reinforcement Learning with Tool Use (ARLT) refers to a class of methods, architectures, and benchmarks that tightly couple reinforcement learning (RL) with the autonomous discovery, invocation, and management of external tools by agents. The fundamental objective is to transcend direct task execution by enabling agents—whether embodied (e.g., robots) or disembodied (e.g., LLM-based digital agents)—to extend their functional reach and reasoning abilities through the adaptive, context-sensitive use of tools. This paradigm draws upon insights from fields ranging from neuroscience (studies of animal tool use) to robotics and contemporary LLMs, and is typically instantiated in environments formalized as multi-turn Markov Decision Processes (MDPs) or partially observable MDPs (POMDPs) in which tool-use actions are first-class and often multi-modal.

1. Conceptual and Algorithmic Foundations

ARLT is grounded in the agentic interpretation of reinforcement learning, where agents are expected to autonomously plan, reason, and interact with complex environments populated by objects, obstacles, and actionable tools. The foundational shift from standard RL is the explicit modeling and evaluation of tool-use capabilities and their generalization. Classic behaviorist paradigms (e.g., trap-tube task studies in animal cognition) are mapped to RL settings by defining environments where the manipulation of tools is essential to achieve otherwise unreachable goals (Wenke et al., 2019). Agents must solve tasks by sequencing tool-use actions with movement, perception, and reasoning, facing variations at the perceptual (appearance), structural (configuration), and symbolic (abstract identity) levels.

Methodologically, ARLT rigorously separates and evaluates:

• Decision processes involving the identification and grasping or invocation of tools;
• Multi-modal observations, e.g., RGB images, proprioceptive feedback, symbolic tool representations;
• Multi-phase learning pipelines, such as joint design-control optimization (Liu et al., 2023), and factored agent architectures that decouple high-level planning from low-level tool formatting (Roth et al., 29 Mar 2025).

To drive generalization, frameworks introduce explicit transfer kernels (𝓕: S → S′) manipulating environment states along orthogonal axes, formulating benchmarks to isolate perceptual, structural, or symbolic reasoning (Wenke et al., 2019). The agent’s reasoning and tool-use policy are thus formalized as policies π(a | s, g, d), where s is the state, g the goal, and d the design or tool context.

2. Core Methodologies and Architectural Patterns

Several architectural patterns characterize contemporary ARLT systems:

• Hierarchical and Factored Architectures: Systems like Agent-as-Tool (Zhang, 2 Jul 2025) and Factored Agents (Roth et al., 29 Mar 2025) explicitly decouple the planning (reasoning about high-level sub-tasks and tool need) from procedural or formatting steps (ensuring well-formed tool calls and outputs). The planner typically operates over natural language or abstracted action spaces, while the toolcaller/memorizer module is fine-tuned for reliable, robust invocation of APIs or device controls. The loss for the integrated system is then

$L_{\text{total}} = L_{\text{ICL}} + L_{\text{mem}}$

allowing each component to be individually optimized for its specific sub-task.

• Multi-Stage RL Pipelines with Tool-Conditioned Control: In robotic settings, e.g., (Liu et al., 2023), the RL pipeline is split into a “designer policy” (producing a specialized tool conditioned on state and goal) and a “controller policy” (manipulating the produced tool to solve the task). The two-stage MDP structure allows rapid adaptation to varied goals by ensuring that tool design is itself a learned, adaptive process.
• Procedural and Synthetic Environment Generation: To address data and benchmark scarcity, frameworks such as RandomWorld (Sullivan et al., 21 May 2025) procedurally generate both tool schemas and synthetic trajectories by sampling over rich type systems. This enables the creation of compositional, multi-step tool-use tasks for both supervised and RL training, greatly improving data diversity and compositional generalization.
• Dynamic Tool Integration with LLMs: In LLM domains, agentic RL frameworks (such as ARTIST (Singh et al., 28 Apr 2025), VerlTool (Jiang et al., 1 Sep 2025), and SFR-DeepResearch (Nguyen et al., 8 Sep 2025)) interleave chain-of-thought reasoning with autonomous tool invocation (search, code execution, SQL/DB access, image processing), supported by structured prompt formats and multi-turn interaction protocols.

3. Reward Structures, Optimization Strategies, and Credit Assignment

ARLT systems require nuanced reward designs and policy optimization strategies tailored to tool use:

• Sparse and Multi-Stage Rewards: Tool-use tasks often yield sparse and/or delayed rewards. Intrinsic motivators (e.g., curiosity-driven reward shaping via Intrinsic Curiosity Modules (Wenke et al., 2019)) and step-wise decomposition (e.g., SWiRL (Goldie et al., 7 Apr 2025)) provide intermediate signals. In SWiRL, the RL objective

$$J(\theta) = \mathbb{E}_{(s, a) \sim \pi_\theta}[R(a \mid s)]$$

is computed across sub-trajectories, providing granular feedback for each tool-use or reasoning action.

• Tool-use Completeness and Structured Rewards: Recent frameworks such as RLTR (Li et al., 27 Aug 2025) introduce reward signals based on tool-use completeness rather than final answer correctness. The planner is optimized to maximize trajectories that invoke the correct sequence of tool calls:

$$R_{\mathrm{comp}} = \frac{1}{N} \sum_{i=1}^{N} \gamma_i(\tau)$$

with $\gamma$ indicating completeness.

• Entropy-Adaptive Rollouts and Advantage Attribution: Algorithms like ARPO (Dong et al., 26 Jul 2025) detect spikes in sequence entropy after tool calls, adaptively branching rollouts where model uncertainty is high. The policy update step accounts for token-level divergence, thus targeting exploration efficiently and optimizing long-horizon tool-use capabilities.
• Masked Observations and Preventing Reward Leakage: As observed in hierarchical agentic systems (Zhang, 2 Jul 2025), observation tokens from tool outputs are masked out during reinforcement loss computation to ensure the agent does not simply memorize or exploit tool outputs, focusing the optimization on planning and reasoning capabilities.

4. Empirical Findings and Benchmarks

A series of controlled experiments across both embodied and LLM-based settings have established several empirical regularities:

• Generalization is the critical challenge: Agents trained only on base environments or with static tool affordances often fail to generalize, especially when faced with perceptual, structural, and symbolic variations in tool characteristics (Wenke et al., 2019). Progressively harder combinations of transfer settings result in degraded performance (e.g., <25% transfer success for the hardest symbolic-structural-perceptual tool-use combinations).
• Diverse and unexpected behaviors can emerge: When exposed to varied tool affordances and minimal reward shaping, agents not only learn basic tool use (e.g., grasp and drag) but also develop “emergent” behaviors like sweeping, hitting, tool-throwing, and error correction (Nguyen et al., 2020). These behaviors reflect adaptability to environmental affordances and partial physical reasoning.
• Sample efficiency and transfer improve with structured policies: Joint designer-controller policies with explicit tool design yield zero-shot generalization and faster fine-tuning on unseen manipulation tasks (Liu et al., 2023). Explicitly decoupled architectures also demonstrate improved planning accuracy and error resilience across standard benchmarks (Roth et al., 29 Mar 2025, Zhang, 2 Jul 2025).
• Real-world and simulated tool use increasingly overlap: Agents trained in simulation with physically grounded tool models and procedural geometry (e.g., adaptive compliance for robotic excavation (Orsula et al., 5 Sep 2025)) generalize their behaviors to real-world hardware, and LLM-based agents are evaluated with real, large-scale tool protocols (MCPVerse (Lei et al., 22 Aug 2025)).
• Benchmarks are rapidly expanding: The introduction of real-world, high-complexity tool-use benchmarks such as MCPVerse (Lei et al., 22 Aug 2025), Multi-modal Agentic Tool Bench (MAT) (Liu et al., 20 May 2025), and NESTFUL (Sullivan et al., 21 May 2025) allows systematic evaluation of both breadth (number and type of tools) and depth (multi-turn, hierarchical reasoning).

5. Practical Architectures, Tool Integration, and Experimental Infrastructures

ARLT research and applications display several engineering and system-level regularities:

• Frameworks for modular, asynchronous, scalable training: VerlTool (Jiang et al., 1 Sep 2025) implements an architecture in which RL training and tool-execution (code, SQL, vision) are decoupled via standardized plugins and asynchronous rollouts. This design promotes scalability, rapid extension to new tools, and near 2× throughput gains over synchronous baselines.
• Unified APIs and lightweight extension: Disposable tools are encapsulated in lightweight Python definitions (VerlTool (Jiang et al., 1 Sep 2025)), facilitating rapid prototyping and community adoption. Such modular plugin architectures are prerequisites for supporting tool diversity in evaluation environments like MCPVerse (Lei et al., 22 Aug 2025).
• Integration of multi-modal capabilities: Contemporary systems integrate not only text-based but also image- and code-based tool calls, often using visual agentic RL fine-tuning to extend chain-of-thought reasoning into visual domains (Liu et al., 20 May 2025).
• Procedural synthetic data for scaling training: End-to-end synthetic environment and trajectory generation (RandomWorld (Sullivan et al., 21 May 2025), SFR-DeepResearch (Nguyen et al., 8 Sep 2025)) are used to overcome bottlenecks in labeled, verifiable training data, facilitating large-scale multi-step RL on challenging, compositional tasks without reliance on costly manual annotation.

6. Future Directions and Open Challenges

The ARLT paradigm brings several methodological opportunities and open questions:

• Towards robust multi-modal, multi-agent, and open-ended tool use: Future research must extend existing frameworks to handle arbitrary modalities (video, audio), collaborative and competitive multi-agent tool use, and open-ended tool creation and composition.
• Improved credit assignment and hierarchical abstraction: Innovations in step-wise and sequence-level reward modeling (e.g., process filtering in SWiRL (Goldie et al., 7 Apr 2025), cross-modal reward shaping in Visual-ARFT (Liu et al., 20 May 2025), length-normalized advantages in SFR-DR (Nguyen et al., 8 Sep 2025)) are needed to support deeper credit assignment over long-horizon tool-use chains and better abstraction.
• Handling uncertainty, tool failures, and environment noise: Systematic mechanisms for detecting, diagnosing, and adapting to errors (e.g., through reflection tokens (Shang et al., 28 Aug 2025), resample-on-correct rollouts (Shang et al., 28 Aug 2025), and error-aware reward penalties) will be essential for safe and reliable deployment.
• Benchmarks and evaluation: The field is rapidly coalescing around large, real-world tool-use benchmarks (e.g., MCPVerse (Lei et al., 22 Aug 2025), NESTFUL (Sullivan et al., 21 May 2025)), requiring RL frameworks that can operate at scale and under strong resource and time constraints.
• Theoretical unification: There remains a need to unify the theoretical analysis of ARLT, addressing the complexity of non-stationary, multi-modal, asynchronous environments, and providing guarantees on generalization, efficiency, and robustness as agents adopt emergent tool-use strategies.

7. Summary Table: Salient Features from Representative ARLT Works

Framework/Paper	Tool Use Focus	Key Methodology/Contribution
(Wenke et al., 2019)	Gridworld tool MDPs	Defines generalization via perceptual/structural/symbolic transfers; evaluates PPO+ICM for tool tasks
(Nguyen et al., 2020)	Physics-based RL	Emergent tool behaviors in MuJoCo; multi-modal inputs; fine-grained reward shaping
(Liu et al., 2023)	Robotic tool design	Two-stage MDP (designer/controller); real robot deployment and tradeoff tuning
(Sullivan et al., 21 May 2025)	Synthetic environment	Procedural compositional data/task generation; type systems for tool schemas
(Roth et al., 29 Mar 2025, Zhang, 2 Jul 2025)	LLM planning+tool use	Factored/hierarchical agent architectures; separate learning for planning and formatting
(Singh et al., 28 Apr 2025, Liu et al., 20 May 2025, Jiang et al., 1 Sep 2025)	LLM+multi-modal tool integration	Unified ARLT frameworks, multi-modal plugins, asynchronous RL, outcome-based RL
(Lei et al., 22 Aug 2025)	Real-world tool benchmarks	552+ tool schemas; evaluates adaptive LLM agents at scale; outcome-based evaluation

This spectrum of methodology, from biologically inspired transfer evaluation to modular, plugin-based infrastructure for open-domain LLM agents, defines the technical and conceptual core of Agentic Reinforcement Learning with Tool Use. The field continues to advance towards robust, generalizable, and scalable systems that autonomously discover, sequence, and adapt tool use to solve dynamic, multi-faceted tasks.