arXiv Machine Learning
Probe-Space Preconditioning for Fast and Stable Zero-Order Training
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SOAP, Muon, and Beyond: Pushing LLM Pretraining Scales
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SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference
arXiv:2606. 10445v1 Announce Type: new Abstract: Semi-structured 2:4 sparsity is widely supported by modern accelerators, providing up to a 2x theoretical speedup.
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Steering the Noise: Turning Random Perturbations into Effective Descent for Memory-Efficient LLM Fine-Tuning
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