AI RESEARCH
Deterministic Differentiable Structured Pruning for Large Language Models
arXiv CS.LG
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ArXi:2603.08065v1 Announce Type: new Structured pruning reduces LLM inference cost by removing low-importance architectural components. This can be viewed as learning a multiplicative gate for each component under an l0 sparsity constraint. Due to the discreteness of the l0 norm, prior work typically adopts stochastic hard-concrete relaxations to enable differentiable optimization; however, this stochasticity can