REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs
Updated 2 h ago · first seen 11 Sept 2026
paper_01M294AHKW5WDXFV7XAS5NW72J
- Published
- 2 Sept 2026
- T1 · 2 h ago
- arXiv
- 2609.01215
- T1 · 2 h ago
Abstract
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…
Authors 2
Riyaaz Shaik, Chandru Venkataraman
Specification
- Paper
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
- arXiv id
- 2609.01215
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
- Published
- 2 Sept 2026
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
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6
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T16
Freshest observation
2 h ago
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- Apple
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Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs (Apple)
apple_ml
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| Apple Machine Learning Research | machinelearning.apple.com/rss.xml | feed | T1· Official | 1 h ago | 1 |
| Apple Machine Learning Research | machinelearning.apple.com/research/refactor-vla-motor-programs | paper_page | T1· Official | 2 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.