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TOPIC · ENTITY #15918

post-training quantization (PTQ)

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RESEARCH · 1 SOURCE · arXiv cs.AI

MPQ: message-passing approach to post-training quantization (arXiv:2610.32127v1)

This arXiv preprint proposes MPQ (Message-Passing Quantization), which frames row-wise post-training quantization as posterior inference over discrete codebooks and preserves soft posterior information across weight blocks using vector approximate message passing before committing to hard codewords via block coordinate descent. The authors report improved perplexity and zero-shot accuracy versus hard-decision baselines (e.g., GPTQ/LDLQ) on 2-bit lattice and 3–4-bit scalar quantization for dense LLMs from the Llama, Qwen, and Mistral families, and provide a reference implementation on GitHub.

7.0

MODELS · 1 SOURCE · arXiv cs.AI

DRQ: Distributionally Robust refinement for low-bit LLM quantization

The arXiv paper shows that minimizing reconstruction loss in weight-only PTQ can fail or even harm downstream performance when input activation distributions shift, and proposes Distributionally Robust Quantization (DRQ), a post-hoc refinement that minimizes worst-case reconstruction loss over constrained input distributions. DRQ adjusts integer codes within existing quantization grids (keeping quantization parameters and inference unchanged) and reportedly improves models quantized by multiple PTQ methods including AWQ, GPTQ, and ParoQuant across both dense and MoE LLMs.

7.0