arxiv
PublishedOctober 7, 2026 at 4:00 AM
QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction
Publisher summary· verbatim
arXiv:2608.13966v2 Announce Type: replace-cross Abstract: As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aware training (QAT) essential for preserving model quality. However, QAT has a structural mis
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Originally published on arxiv ↗