Feiyu Wang

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization

ReQuant is introduced, a backpropagation-free fixed-grid refinement procedure that takes an existing quantized model as a feasible starting point and iteratively revisits its discrete weight assignments on the fixed quantization grid, and turns the initially fixed PTQ output into an iteratively optimizable discrete solution.