arXiv AI
Aug 5

Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss

arXiv:2608. 03796v1 Announce Type: cross Abstract: Small language models are often the only option for deployment under tight latency, cost, and on-premises constraints, but they are rarely trained from scratch: a compressed model is usually recovered through knowledge distillation (KD).

By Bakbergen Ryskulov, Iker Garc\'ia-Ferrero, David Montero, David Jansen, Ali Hashemi, Jezabel R. Garcia, Antonio Tiene, Rom\'an Or\'us
arXiv AI
Jul 16

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

arXiv:2607. 13124v1 Announce Type: cross Abstract: Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compressed checkpoints can collapse on the free-form generation that deployment actually requires.

By Qingyu Zhang, Qianhao Yuan, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Xiang Li, Ming Xu, Jiarui Li, Xiuyin Zhao
arXiv AI
6d ago

AutoCompress: Automated Hardware-Guided Model Compression

AutoCompress is a hardware‑guided model compression framework that integrates latency, throughput, and memory constraints directly into differentiable architecture search. It jointly optimizes global and per‑layer structures to produce heterogeneous architectures, using serving‑engine cost models validated against real end‑to‑end measurements. In evaluations on long‑context coding, datacenter serving, and in‑game interaction, AutoCompress outperforms the Minitron pruning framework, improving average accuracy by up to 6.57 percentage points on RTX 5070 and compressing Nemotron 3.5 Super from 120B to 74B parameters while retaining 95% of its benchmark score.

By Sheldon Liang, Ali Taghibakhshi, Saurav Muralidharan, Sharath Turuvekere Sreenivas, Ashwath Aithal, Pavlo Molchanov, Nima Tajbakhsh