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cs.LG2026
Enhancing Post-Training Quantization via Future Activation Awareness
Zheqi Lv, Zhenxuan Fan, Qi Tian +2
Post-training quantization (PTQ) is a widely used method to compress large language models (LLMs) without fine-tuning. It typically sets quantization hyperparameters (e.g., scaling…
cs.LG2025
Scaling and Transferability of Annealing Strategies in Large Language Model Training
Siqi Wang, Zhengyu Chen, Teng Xiao +5
Learning rate scheduling is crucial for training large language models, yet understanding the optimal annealing strategies across different model configurations remains challenging…