40 citations · 59 across the 13 of their papers we have counts for
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cs.LG2026
Enhancing LLM Metacognition via Cognitive Pairwise Training
Weitao Li, Hao Zhou, Xuanyu Lei +11
Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to give confident answers when…
cs.LG2025
iFairy: the First 2-bit Complex LLM with All Parameters in
Feiyu Wang, Guoan Wang, Yihao Zhang +7
Quantization-Aware Training (QAT) integrates quantization into the training loop, enabling LLMs to learn robust low-bit representations, and is widely recognized as one of the most…
cs.LG2024★ 1 cited
Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview
Yanshu Wang, Tong Yang, Xiyan Liang +5
This paper provides a comprehensive overview of the principles, challenges, and methodologies associated with quantizing large-scale neural network models. As neural networks have…