Author

Tian-Yi Zhang

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#machine learning Preprint Sep 2026

QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs

Large language model (LLM) inference is increasingly moving toward lower precision to realize the throughput of hardware accelerators, but aggressive post-training quantization (PTQ) can degrade model quality. We present QATFactory, an open-source framework for deployment-aligned quantization-aware distillation (QAD) a...

Wei-Li Xu, Ji-Sen Li, Yu-Qing Jian et al. · 0 citations

#natural language process... Preprint Sep 2026

Osprey: Target-agnostic Pre-training Makes Stronger Drafters in Speculative Decoding

Speculative decoding is critical for accelerating LLM inference. However, the speedup is fragile: drafters are typically trained against a narrow distribution for a single target model, and their acceptance rate collapses under workload shifts. This is a striking inversion of modern LLM development, where target models...

Fengxiang Bie, Yu-Qing Jian, Yi-Fan Yu et al. · 0 citations

Preprint Aug 2026

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

QUASAR is introduced, a QAT method that continuously performs lightweight, loss-aware reconstruction in the training loop to lower the loss floor and improve the resulting low-bit model, establishing QUASAR's objective as a principled optimization target.

Vincent Counathe, Ben Athiwaratkun, C. De Sa et al. · 1 citation