Softmax Reparameterization for Efficient Output-Head Quantization

This post-training method preserves full-precision softmax distributions in small language models by selecting a functionally equivalent output head before quantization. Using a one-dimensional search and rank-one correction, it mitigates the high inference costs of large vocabularies in models like Phi-4-mini.