arXiv:2608.23816v1 Announce Type: new
Abstract: Quantized fine-tuning (QLoRA) saves memory but not time. It dequantizes every 4-bit weight on the fly, so it trains more slowly than fp16 LoRA. We pres...
By Md Romyull Islam
arXiv:2607. 02893v1 Announce Type: new Abstract: Low-bit quantization shrinks language models but treats precision as a single global hyper-parameter: every weight uses the same bit-width.
By Hamish Ogilvy
arXiv:2608.23601v1 Announce Type: cross
Abstract: EDA flow parameter tuning is critical for quality-of-results~(QoR), yet the parameter space is large, tightly coupled, and full evaluations are prohi...
By Kunlong Li, Shangshang Yao, Su Zheng, Lingli Wang
arXiv:2608. 01041v1 Announce Type: cross Abstract: Machine-learning predictors estimate processor performance far faster than cycle-level simulation.
By Yanxin Zhang, Shayne Wadle, Yuxuan Xiong, Zheyu Fu, Trivikram Krishnamurthy, Karu Sankaralingam
arXiv:2610.16451v1 Announce Type: cross
Abstract: Fine-tuning large language models at scale is increasingly limited by the memory cost of backpropagation. Zeroth-Order (ZO) optimizers reduce this bu...
By Xuanlin Yang, Jia Li, Zhouchen Lin
arXiv:2607. 16268v1 Announce Type: new Abstract: Adaptive optimizers such as Adam and AdamW apply the same update rule regardless of whether training is in a chaotic early phase or near convergence.
By Ali Sultonov