LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models

In three linesLEAP introduces end-to-end unstructured pruning for LLMs via per-weight Bernoulli-Gumbel-sigmoid relaxation. Across five model families (0.5B–8B) at 50–60% sparsity, LEAP improves average zero-shot accuracy by +2.59 points over ADMM baseline.

Summary generated by Claude — human-verified