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arXiv Machine Learning By Jason Jabbour, Dong-Ki Kim, Max Smith, Jay Patrikar, Radhika Ghosal, Youhui Wang, Ali Agha, Vijay Janapa Reddi, Shayegan Omidshafiei

Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered

Read the original on arXiv Machine Learning →

The paper investigates pruning for Vision‑Language‑Action (VLA) models, finding that it causes severe performance loss and safety violations. Spectral analysis reveals structural weight properties that explain this fragility. The authors propose GLUESTICK, a post‑pruning recovery method that interpolates between dense and pruned weights, restoring up to 100% of success rate and safety while saving memory and speeding inference.

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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