arXiv Machine Learning October 8, 2026 By Eliyahu Levy, Adam Teman, Yoni Pugachov

TR-PTQ: High-Accuracy Integer-Only Transformer Post Training Quantization via Taylor Region Reformulation

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TR-PTQ is a post‑training quantization method that enables high‑accuracy, integer‑only inference for transformer models. It identifies learned scale parameters in LayerNorm and compounded approximations in GELU as the main sources of accuracy loss, while SoftMax is robust to aggressive quantization. By reformulating these operations with shared Taylor Region exponential and logarithm primitives and a calibration‑free, outlier‑aware optimization for LayerNorm, TR‑PTQ eliminates the need for floating‑point units for nonlinearities and achieves less than 1.5% absolute accuracy degradation on vision and language benchmarks.

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