arXiv Machine Learning August 17, 2026 By Vincent Counathe, Ben Athiwaratkun, Christopher De Sa, Tianyi Zhang

QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction

Read the original on arXiv Machine Learning →

arXiv:2608. 13966v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning

SchurQuant: Groupwise Discrete Optimization for Layer-Wise LLM Quantization

Understanding Quantization-Aware Training: Gradients at Quantized Weights Bias to the Low-Loss Basin

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