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cs.CL2026
GPTQ: Improving LLM Post-Training Quantization with Generalized Gradient Compensation
Ruikang Liu, Haoli Bai, Yuxuan Sun +8
Post-training quantization (PTQ) is a practical approach to reducing the memory and computational footprint of large language models (LLMs) without retraining. GPTQ-based methods h…
cs.CL2026
AdaMTP: An Adaptive Training Paradigm for Multi-Token Prediction
Ziqiang Cui, Han Shi, Bowei He +8
Multi-Token Prediction (MTP) has emerged as an effective paradigm that augments a shared Large Language Model backbone with auxiliary heads, training the model to predict several f…
cs.CL2026
Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats
Manyi Zhang, Ji-Fu Li, Zhongao Sun +4
Microscaling Floating-Point (MXFP) has emerged as a promising low-precision format for large language models (LLMs). Despite various post-training quantization (PTQ) algorithms bei…