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cs.LG2026
GA-SVD: Generation-aware Singular Value Decomposition for LLM Compression
Shengkun Tang, Cong Zeng, Zongfang Liu +2
Large language models (LLMs) demonstrate strong generalization across a broad range of reasoning and generation tasks, but their large parameter counts impose substantial memory an…
cs.LG2026
Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training
Jingwei Zuo, Cong Zeng, Ilyas Chahed +6
The training paradigm of large language models has shifted from traditional one-pass training to multi-epoch training, as reasonable reuse of limited high-quality data can improve…
cs.LG2025
MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics
Bowei Guo, Shengkun Tang, Cong Zeng +1
Diffusion models are renowned for their generative capabilities, yet their pretraining processes exhibit distinct phases of learning speed that have been entirely overlooked in pri…