BWLA (LLM quantization): current frontier — recent, not yet superseded in the knowledge base. 0 paper(s) critique it, 0 beat it on benchmarks — not ranked as a superseded baseline.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.

Tracked

BWLA

BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs

LLM quantization · first seen May 1, 2026

current frontier — recent, not yet superseded in the knowledge base

0 papers critique it · 0 beat it on benchmarks

Newer alternatives

Recent methods in the same sub-problem, not yet superseded in the knowledge base.

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