SOURCE-LINKED INTELLIGENCE

A Target-Centric Survey of Quantization-Aware Training

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

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-centri

Read original source ↗ Open in workspace

recordType

paper

region

Global