Modular Skill Internalization for Agentic Foundation Models

Augmenting large language models with reusable procedural skills via in-context prompting suffers from an inherent bottleneck in capability acquisition. Because a model must infer execution procedures entirely from textual descriptions at runtime, the efficacy of injected skills remains strictly bounded by its pre-existing instruction-following and context-comprehension abilities. Furthermore, in-context skill descriptions can only steer model behavior through conditioning on the current input, whereas the model's learned capabilities are instantiated in its parameters. This creates a fundamental mechanistic mismatch: textual skill injection asks the model to reconstruct a new procedure through transient, context-dependent computation, rather than incorporating that procedure into the parameterized transformations that constitute its learned capabilities. To address this limitation, we introduce Modular Skill Internalization (MSI), a framework that compiles declarative skill specifications into modular, lightweight parameter updates. For each skill, MSI trains an independent parameter module from synthetic skill-specific demonstrations, such that attaching the module at inference time equips the base model with the corresponding procedural capability without requiring the original skill description. These modules are learned independently with behavioral anchors to encourage preservation of the backbone’s existing behavior, enabling skills to be acquired, stored, and invoked as modular parameterized capabilities. Extensive evaluations across four agentic tasks and four foundation backbones demonstrate that MSI consistently outperforms in-context prompting in task performance.