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

Paper · 2026 · Zhixiong Zhao, Zukang Xu and Dawei Yang

Summary

Binarizing LLM weights to 1 bit greatly reduces storage and bandwidth. However, earlier binarization methods such as BiLLM and ARB-LLM keep activations in high precision, because activation outliers and heavy tails make low-bit activation quantization fail. This forces weight dequantization at inference and limits real speedups.

The authors (Houmo AI) introduce BWLA, a retraining-free post-training quantization framework for 1-bit weights combined with low-bit activations (W1AX). It has two components:

  • Orthogonal-Kronecker Transformation (OKT) learns an orthogonal rotation, fitted with a gradient-free EM-style procedure. The rotation is built cheaply as a Kronecker product of two small orthogonal matrices. It reshapes bell-shaped per-channel weights into symmetric two-peaked distributions that suit the ±1 codebook. Because the matrix is orthogonal, applying it to activations preserves model outputs while flattening outliers.
  • Proximal SVD Projection (PSP) adds a small truncated-SVD low-rank correction that absorbs remaining outliers.

Experiments on LLaMA and Qwen3 models show BWLA is the first pure PTQ method to stay accurate in this regime. This makes end-to-end accelerated binary LLM inference practical.

Key points

  • On Qwen3-32B with 6-bit activations, BWLA reaches a Wikitext2 perplexity of 11.92, versus about 38 for the previous best method. Its gains on five zero-shot QA tasks exceed 70%.
  • On an NVIDIA RTX A6000, a W1A8 LLaMA2-13B runs at 77.31 tokens/s, compared with 23.70 for FP16 and 55.35 for W4A4 OSTQuant. This is a 3.26× speedup over FP16, and parameter memory drops from 23.7 GB to 3.94 GB.
  • Making the Kronecker factors more symmetric cuts OKT overhead from roughly 1 bit to 0.01 bit, at a cost of about 3% zero-shot accuracy. A PSP rank ratio of 0.005 gives the best cost/quality balance.
  • Under W1A6, BWLA achieves a WikiText2 perplexity more than 70% below BiLLM's and more than 50% below ARB-LLM's. Quantization takes only about 25 minutes longer than BiLLM on average.
  • Stated limitations: accuracy degrades at W1A4, the method relies only on linear rotations, and it currently supports only integer formats (formats such as MXFP4 are not yet covered).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 worksEngineering · 188 worksElectrical and Electronic Engineering · 33 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,894 worksEfficient models & compression · 297 works

Cite

Cite as Zhixiong Zhao, Zukang Xu and Dawei Yang. 2026. BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.880

@inproceedings{zhao2026bwla,
  title = {{BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs}},
  author = {Zhixiong Zhao, Zukang Xu and Dawei Yang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.880},
  url = {https://doi.org/10.18653/v1/2026.acl-long.880}
}

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