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cs.LG2026
Small LLMs: Pruning vs. Training from Scratch
Yufeng Xu, Taiming Lu, Kunjun Li +3
Pruning promises a shortcut to strong small language models. In this work, we examine this promise by pruning Llama-3.1-8B at pruning ratios of 0.5--0.8 with six methods spanning d…
cs.CV2026
i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu +4
Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific modeling and data choices: state…
cs.LG2026
Strong Teacher Not Needed? On Distillation in LLM Pretraining
Taiming Lu, Zhuang Liu
Knowledge distillation generally assumes a strong-to-weak relationship where stronger teachers yield better students. In this work, we examine this assumption about distillation in…