Is Layer-Wise High-Impact Parameter Ratio Optimization superseded?

Layer-Wise High-Impact Parameter Ratio Optimization (LLM quantization): current frontier — recent, not yet superseded in the knowledge base. 0 paper(s) critique it, 0 beat it on benchmarks — not ranked as a superseded baseline. Sub-problem: cluster led by RTN. Newer alternatives in the same sub-problem include STaR-Quant, Timestep-Aware SVDQuant-GPTQ, BWLA, Bit-by-Bit, Benford-Quant.

LLM quantization

Layer-Wise High-Impact Parameter Ratio Optimization

Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models

On the frontierfirst seen Nov 21, 2025

Current frontier — recent, not yet superseded in the knowledge base

0 papers critique it · 0 beat it on benchmarks

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.

STaR-QuantSTaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models
Jun 3, 2026
Timestep-Aware SVDQuant-GPTQTimestep-Aware SVDQuant-GPTQ for W4A4 Quantization of Wan2.2-I2V
May 26, 2026
BWLABWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
May 1, 2026
Bit-by-BitBit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs
Apr 9, 2026
Benford-QuantBenford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models
Jan 29, 2026
HestiaHESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs
Jan 28, 2026
Layer-Wise High-Impact Parameter Ratio OptimizationLayer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models
Nov 21, 2025
RobuQRobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
Sep 28, 2025