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arXiv AI By Salem Ameen, Sunil Vadera

Damage-Aware Bandit Pruning for Vision and Language Transformers

Read the original on arXiv AI →

The paper introduces a structured post‑training pruning method for vision and language transformers called Damage‑Aware Bandit Pruning. It treats the selection of functional units (attention heads and MLP channel groups) as a multi‑armed bandit problem, using paired damage (masked loss minus base loss) as a reward to guide either UCB or Thompson Sampling policies. Experiments on a range of models (GPT‑2, OPT, Pythia, Qwen2.5, SmolLM2, ViT‑B/16, DeiT‑Tiny, Swin‑Tiny) show that the bandit approaches generally reduce degradation compared to budgeted‑greedy baselines, with statistically significant improvements in most comparisons.

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 AI.

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