A Target-Centric Survey of Quantization-Aware Training

• URL: http://arxiv.org/abs/2608.29667v1
• Date: Sun, 30 Aug 2026 09:04:47 GMT
• Title: A Target-Centric Survey of Quantization-Aware Training
• Abstract summary: Quantization-Aware Training (QAT) techniques have emerged as a promising solution to address these challenges.<n>QAT techniques explicitly simulate quantization effects during model training, yielding low-bit models that achieve accuracy comparable to their full-precision counterparts.
• Score: 53.52583463060327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of LLMs incurs prohibitive memory footprints and intensive computational demands. Quantization-Aware Training (QAT) techniques have emerged as a promising solution to address these challenges by explicitly simulating quantization effects during model training, yielding low-bit models that achieve accuracy comparable to their full-precision counterparts. In this work, we provide a target-centric survey of QAT, aimed at clarifying both its theoretical foundations and its evolving implementation landscape. We systematically review existing QAT methods through a target-centric taxonomy and synthesize cross-target differences in error characteristics, numerical formats, and strategy transferability. We further summarize QAT evaluation paradigms and discuss challenges in optimization and deployment, outlining potential directions for future research.

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