In-Tool Learning in AI Systems

• In-tool learning is a paradigm where AI agents use external tools to access unbounded knowledge beyond their model parameters.
• It employs structured tool queries and dynamic tool selection to ensure scalability and improved factual recall in complex tasks.
• Empirical studies show its effectiveness in robotics and language tasks, while highlighting challenges in tool retrieval and security.

In-tool learning is a paradigm in which artificial agents—including LLMs or robotic systems—are trained not only to solve problems end-to-end but to leverage external tools, APIs, or information retrieval systems as integral extensions of their capabilities. Unlike in-weight learning, where all knowledge is stored within model parameters and accessed through direct recall, in-tool learning equips the agent to interact with external resources in a structured manner, effectively decoupling knowledge storage from usage. This approach enables scalability, modularity, and adaptability in both language-centric and embodied (robotic) agents, fundamentally altering the limits and workflows of AI systems.

1. Theoretical Foundations and Scalability

In-tool learning is contrasted with in-weight learning, where all factual or procedural knowledge must be stored within a model's finite parameter set. The theoretical capacity of in-weight knowledge retention is bounded by the model size: the number of facts $|F|$ that can be reliably recalled is limited by a parameter lower bound of

$$P \geq \frac{|\mathcal{N}|}{b} \sum_{a \in \mathcal{A}} \log_2 |\mathcal{V}_a|$$

where $|\mathcal{N}|$ is the set of names/entities, $b$ is the number of bits per parameter, and $\mathcal{V}_a$ the value set for attribute $a$ (Houliston et al., 28 Aug 2025). This leads to a linear scaling requirement, rendering purely in-weight storage impractical for large fact databases.

In-tool learning eliminates this bottleneck. If a model is trained to construct structured tool queries (e.g., API or database calls), then, as shown in both construction and proof, even small transformers can access an unbounded set of external facts: the information resides in an efficient and extendable external resource, and the model only needs to learn the query format and compositional interface.

Empirical validation demonstrates that, as the number of facts exceeds the model’s memorization limit, performance of the in-weight strategy falls away rapidly, while in-tool models maintain high recall by generating tool queries (structured string templates) and parsing tool responses (Houliston et al., 28 Aug 2025).

2. Methodological Advances and Architectures

Methodologies for in-tool learning differ depending on modality (language, robotics), but share key architectural elements:

• Controller-perceiver architectures: Foundation models act as controllers that interpret user queries, decompose them into subtasks, and decide which tools to invoke, while perceivers process tool outputs and feed back into the agent’s state (Qin et al., 2023).
• Language-conditioned policy learning: In robotic or embodied agents, policies are conditioned on rich semantic input about tools, including natural language descriptions of geometry and affordances, with meta-learning algorithms (e.g., Reptile-inspired updates) enabling rapid adaptation to new tools (Ren et al., 2022).
• Dynamic tool selection and invocation: Sequence models are trained to select tools and generate API calls as part of the output—either as special tokens (e.g., <tool_call>), code snippets for execution, or structured queries for retrieval (Zheng et al., 11 Mar 2024, Ding et al., 17 Feb 2025).
• External feedback loops: Reinforcement learning with execution feedback (RLEF) incorporates the success or failure of tool calls directly in the reward structure, refining the agent’s strategy for when and how to use tools (Qiao et al., 2023, Yu et al., 10 Oct 2024, Feng et al., 15 Apr 2025).

The adoption of graph-based representations (ToolNet (Liu et al., 29 Feb 2024)), parallel invocation via DAG scheduling (DTA-Llama (Zhu et al., 21 Jan 2025)), and code-centric reasoning (ToolCoder (Ding et al., 17 Feb 2025)) further extend the architectural scope and enable handling of large-scale tool libraries.

3. Experimental Evidence and Practical Outcomes

Empirical results across domains demonstrate marked gains from in-tool learning:

• Factual recall scaling: Controlled experiments show that as the number of facts increases, in-tool models vastly outperform in-weight models, with parameter requirements flattening instead of growing linearly after exceeding a memorization threshold (Houliston et al., 28 Aug 2025).
• Robotics (tool manipulation): On manipulation tasks such as pushing, lifting, sweeping, and hammering, policies that leverage language-conditioned meta-learning with natural language tool descriptions adapt significantly faster to unseen tools compared to those trained with only visual inputs or classical meta-learners (Ren et al., 2022).
• API and tool-based language tasks: On benchmarks such as ToolBench, API-Bank, and RestBench, models trained for structured tool invocation exhibit higher accuracy, completion rates, and robustness—especially in scenarios with many tools or complex multi-step requirements (Ding et al., 17 Feb 2025, Liu et al., 29 Feb 2024, Zhu et al., 21 Jan 2025).

A consistent theme is improved generalization. In-tool policies, once they've learned the logic for constructing queries or invoking tools, generalize more reliably to new data or tool libraries, unlike memorization-based strategies which exhibit sharp performance drops on out-of-distribution queries (Houliston et al., 28 Aug 2025).

4. Challenges, Limitations, and Security

Despite these benefits, critical challenges remain:

• Tool selection and retrieval: As tool libraries scale, effective retrieval and selection become essential. Hierarchy- and context-aware reranking (ToolRerank (Zheng et al., 11 Mar 2024)) improve precision especially for unseen tools.
• Behavior planning and long-horizon reasoning: Complex tool use often requires the decomposition of user intent into multi-step plans, temporal coordination, and the preservation of key state across steps. LLMs frequently struggle with robust behavior planning, with limited proficiency in integrating external outputs over long horizons (Ye et al., 1 Jan 2024, Chen et al., 17 May 2025).
• Over- and under-use of tools: RL-based strategies and execution feedback (e.g., TRICE (Qiao et al., 2023)) help mitigate both the excessive reliance on external tools for simple tasks (which can propagate errors) and the underuse of tools when needed for complex queries.
• Security risks: Tool-augmented LLMs open additional attack vectors, notably through the possibility of tool output misuse, concealed tool invocations (deceptive threats), and failure to warn users of potentially untrusted or dangerous tool outputs. Safety benchmarks reveal that even advanced models display significant variability in disclosure and risk attribution across scenarios and languages (Liu et al., 21 May 2025).
• Model scaling paradox: Simply increasing model size does not necessarily enhance in-tool learning capabilities, and can exacerbate issues such as format non-alignment and verbosity, which hinder successful tool invocation (Ye et al., 1 Jan 2024).

5. Extensions and Future Directions

Current research highlights several priorities for advancing in-tool learning:

• Autonomous and compatibility-aware learning: Allowing agents to self-select appropriate training data according to their current competence yields more substantial gains than indiscriminately enlarging training sets (Chen et al., 17 May 2025).
• Rationale-rich and structured reasoning: Integrating explicit chain-of-thought generation and in-code planning (e.g., modular Python scaffolds) helps overcome shortcut behavior and supports generalization (Ding et al., 17 Feb 2025, Chen et al., 17 May 2025).
• Continual adaptation: Through mechanisms such as episodic replay and self-evolution (ToolACE-DEV (Huang et al., 12 May 2025)), LLMs can maintain and extend tool-use capabilities as tools and user requirements change, without catastrophic forgetting or dependence on advanced teacher models.
• Reward and reinforcement signal design: Sophisticated reward shaping—including format, argument correctness, rejection, and group normalization (ToolRL (Qian et al., 16 Apr 2025), StepTool (Yu et al., 10 Oct 2024))—drives more robust, scalable, and generalizable tool-integration policies, particularly for compositional and ambiguous tasks.
• Hybrid neuro-symbolic architectures: Embedding neural reasoning within structured, symbolic tool-based execution pipelines enhances the spectrum of tasks addressable (e.g., mathematical computation and symbolic reasoning (Feng et al., 15 Apr 2025)), while also providing pathways for interpretability and debugging.

6. Implications for AI System Design

The adoption of in-tool learning principles represents a fundamental shift in AI system architecture. By decoupling the locus of knowledge from a model’s parametric memory and enabling structured, compositional tool use, agents can be designed with provable scalability for factual tasks, improved adaptability to environment and tool changes, and greater modularity for future expansion.

For applications ranging from embodied robotics to intelligent digital assistants and scientific computation, this offers a blueprint for developing robust, extensible, and safer AI systems that can keep pace with rapidly evolving user and domain demands. Furthermore, theoretical results indicate that such architectures are not only practically effective but offer rigorous guarantees on their scalability and maintainability in the presence of growing and evolving knowledge bases (Houliston et al., 28 Aug 2025).