Researchers Develop Framework for Efficient Skill Grounding in Robots Using Small Language Models
Researchers have introduced RECENT, a code-refactoring-based framework that allows small language models (sLMs) to effectively ground reusable skills in embodied robotic agents. The work addresses a key challenge in robotics: skills trained for one robot or environment often fail when transferred to different embodiments or settings, and deploying large language models on-device is typically impractical. Accepted to ICML 2026, RECENT demonstrates that targeted code refactoring — rather than full code regeneration — can close the performance gap between sLMs and much larger models.
RECENT (Refactoring-Centric Agent Framework) tackles the problem of skill grounding in embodied AI, where agents must adapt pre-built skills to new robots or environments without relying on computationally expensive large language models. The core insight is to represent skills as executable code and decouple their semantic intent — encoded in the control structure — from the embodiment- and environment-specific execution bindings. Rather than regenerating code from scratch when conditions change, RECENT performs localized refactoring, modifying only the execution bindings while preserving the skill's underlying logic. This approach is particularly suited to dynamic, partially observable environments where on-device inference with sLMs is the only feasible option. The framework was evaluated across multiple robot embodiments and diverse skill grounding scenarios requiring long-horizon control. RECENT outperformed all other sLM-based Code-as-Policies methods tested and matched the task performance of LLM-based approaches. The paper has been accepted to the International Conference on Machine Learning (ICML) 2026.
What's missing
Computational cost comparisons (latency, memory footprint) between RECENT and LLM-based baselines are not described in the abstract. It is also unclear how RECENT performs in fully real-world (as opposed to simulated) deployments, or whether the skill grounding degrades under more extreme distribution shifts than those tested.
What different sources said
- arXiv cs.AICenter
Language-based Trial and Error Falls Behind in the Era of Experience
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