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cs.LG2026
A Target-Centric Survey of Quantization-Aware Training
Jiamin Song, Mengjie Zhao, Zijing Wang +6
The rapid development of LLMs incurs prohibitive memory footprints and intensive computational demands. Quantization-Aware Training (QAT) techniques have emerged as a promising sol…
cs.LG2026
ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning
Yongkang Liu, Zijing Wang, Mengjie Zhao +7
This work presents \textsc{ChunkFT}, a memory-efficient fine-tuning framework that reformulates full-parameter fine-tuning around a dynamically activated working set. \textsc{Chunk…
cs.LG2026
SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
Yongkang Liu, Xing Li, Mengjie Zhao +7
As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large language models. Low-rank Adaptation…