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cs.CL2026
The Devil Is in the Reconstruction Loss Scale: Rethinking Optimization in LLM Quantization
Chao Li, Shigeng Wang, Anbang Yao
Post-training quantization (PTQ) methods typically use sequential quantization that partitions a pre-trained LLM into a series of units (e.g., transformer blocks), with one unit qu…
cs.CL2026
Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLMs through the Lens of 1.58-Bit Quantization
Shigeng Wang, Chao Li, Yangyuxuan Kang +2
We propose ScaleQ-1.58, a scalable ternary post-training quantization (PTQ) framework for reasoning LLMs. Its core insight stems from an empirical finding: although modern LLMs are…
cs.CL2026
CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs
Shigeng Wang, Chao Li, Yangyuxuan Kang +2
In this paper, we present CAT-Q, Cost-efficient and Accurate Ternary Quantization, for compressing and accelerating LLMs. Unlike existing state-of-the-art ternary quantization meth…