4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.DC2026
Scheduling the Unschedulable: Taming Black-Box LLM Inference at Scale
Renzhong Yuan, Yijun Zeng, Xiaosong Gao +3
When output token counts can be predicted at submission time (Gan et al., 2026), client-side scheduling against a black-box LLM API becomes semi-clairvoyant: decisions condition on…
cs.LG2024★ 4 cited
LoRA-GA: Low-Rank Adaptation with Gradient Approximation
Shaowen Wang, Linxi Yu, Jian Li
Fine-tuning large-scale pretrained models is prohibitively expensive in terms of computational and memory costs. LoRA, as one of the most popular Parameter-Efficient Fine-Tuning (P…