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cs.LG2026
AsymVLM: Asymmetric Token Pruning for Efficient Vision-Language Model Inference
Yilin Feng, Ahmed Burak Gulhan, Mahmut Taylan Kandemir
Vision-Language Models (VLMs) process thousands of visual tokens per image alongside comparatively few text tokens, yet existing compression methods treat both modalities uniformly…
cs.LG2026
Pretraining large language models with MXFP4 on Native FP4 Hardware
Musa Cim, Sarthak Arora, Poovaiah Palangappa +4
Why does full-pipeline FP4 training of large language models often diverge, even when forward activations and activation gradients remain stable? We address this question through a…