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arXiv Machine Learning By Taegeun Yang, Youngju Na, Yoonki Cho, Sung-Eui Yoon

Same Scene, Different Task: Skill Alignment for Compositional Generalization in VLAs

Read the original on arXiv Machine Learning →

The paper introduces CRAFT, a method for improving compositional generalization in vision‑language‑action (VLA) models. It addresses the issue where models rely on visual shortcuts during fine‑tuning, leading them to execute demonstrated skill combinations that match observations rather than the instructed ones. By training with counterfactual instruction–observation pairs and transferring supervision through reusable skill representations, CRAFT enhances success on unseen skill combinations while preserving performance on demonstrated ones across multiple VLA models and benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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