# REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs

DevFeed: [REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs](<https://devfeed.tech/articles/refactor-vla-unsupervised-library-learning-of-typed-motor-programs-6732.md>)

Original publisher: [Read original article](<https://machinelearning.apple.com/research/refactor-vla-motor-programs>)

Published: 2026-09-02T00:00:00Z

Content type: article

Language: en

Sources: [Apple Machine Learning Research](<https://devfeed.tech/sources/apple-machine-learning-research.md>)

Topics: [World models](<https://devfeed.tech/topics/world-models.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [models](<https://devfeed.tech/tags/models.md>), [skills](<https://devfeed.tech/tags/skills.md>), [training](<https://devfeed.tech/tags/training.md>), [world-model](<https://devfeed.tech/tags/world-model.md>)

## AI overview

REFACTOR-VLA is a vision-language-action system that learns reusable typed motor-program skills through alternating world-model-based clustering and policy optimization. On LIBERO, the article reports that scaling the world model reduced performance across all four benchmark suites, while an InfoNCE auxiliary loss improved skill clustering.

## Source excerpt

Most current vision-language-action (VLA) models--such as OpenVLA, π0, RT-2, and RDT-1B--are "monolithic." This means they generate raw motor commands or very short sequences of actions, without organizing behaviors into reusable, well-defined abstractions. As a result, these models perform poorly on long-horizon (multi-step) tasks, and it's difficult to interpret what they have learned. Existing approaches for discovering skills often avoid the core problem of deciding when two action sequences are "behaviorally equivalent." For example, AtomicVLA and AtomSkill group action sequences by...