Showing cs.CLShow all
3 papers · 1 filter
cs.CL2026
Rethinking Local Learning: A Cheaper and Faster Recipe for LLM Post-Training
Hengyu Shi, Tianyang Han, Peizhe Wang +3
LLM post-training typically propagates task gradients through the full depth of the model. Although this end-to-end structure is simple and general, it couples task adaptation to f…
cs.CL2026
Chain-based Distillation for Effective Initialization of Variable-Sized Small Language Models
Boyu Shi, YiCheng Jiang, Chang Liu +3
Large language models (LLMs) achieve strong performance but remain costly to deploy in resource-constrained settings. Training small language models (SLMs) from scratch is computat…
cs.CL2026
Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions
Boyu Shi, Chang Liu, ChuanBao Gao +2
Layer pruning efficiently reduces Large Language Model (LLM) computational costs but often triggers sudden performance collapse. Existing representation-based analyses struggle to…