Author

Chao Li

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#artificial intelligence Preprint Oct 2026

The Devil Is in the Reconstruction Loss Scale: Rethinking Optimization in LLM Quantization

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 quantized at each stage. State-of-the-art PTQ methods are predominantly learning-based, optimizing auxiliary quantization parameters (e.g., sc...

Chao Li, Shi-Geng Wang, An-Bang Yao · 0 citations

Preprint Aug 2026

Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLMs through the Lens of 1.58-Bit Quantization

ScaleQ-1.58 is proposed, a scalable ternary post-training quantization (PTQ) framework for reasoning LLMs that demonstrates strong generalization across tasks of varying difficulty levels, including mathematics, coding and scientific logic reasoning, as well as commonsense reasoning and basic language generation.

Shi-Geng Wang, Chao Li, Yangyuxuan Kang et al. · 3 citations