Efficient Skill Grounding via Code Refactoring with Small Language Models
Effective skill grounding is essential for deploying reusable skills in embodied agents, as even minor embodiment or environmental differences can render an entire skill incompatible. This challenge is particularly pronounced in embodied settings, where agents must operate in dynamic, partially observable environments without access to large language models (LLMs). In this setting, reliance on LLMs is impractical, while small language models (sLMs) remain insufficient for the effective skill grounding required for reliable long-horizon control. We present RECENT, a refactoring-centric agent framework that enables efficient skill grounding with sLMs by decoupling skill semantics from embod...
| Authors | Sera Choi, Wonje Choi, Saehun Chun et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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