From the 1 of 7 linked papers with an AI index.
7 papers
Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion
Fengzhuo Zhang, Zhuoran Yang, Dirk Bergemann
The paper studies when users of large language models should use expensive supervised fine-tuning versus lightweight in‑context learning, considering how other users' choices creat…
INFUSER: Influence-Guided Self-Evolution Improves Reasoning
Siyu Chen, Miao Lu, Beining Wu +7
Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend…
Why Muon Outperforms Adam: A Curvature Perspective
Shuche Wang, Fengzhuo Zhang, Jiaxiang Li +2
Muon improves training efficiency over Adam in large language-model training by about two times, but the local geometric source of this advantage remains unclear. Our work takes a…
Demystifying the Slash Pattern in Attention: The Role of RoPE
Yuan Cheng, Fengzhuo Zhang, Yunlong Hou +5
Large Language Models (LLMs) often exhibit slash attention patterns, where attention scores concentrate along the -th sub-diagonal for some offset . These patterns play a k…
Error Analyses of Auto-Regressive Video Diffusion Models: A Unified Framework
Jing Wang, Fengzhuo Zhang, Xiaoli Li +5
Auto-Regressive Video Diffusion Models (AR-VDMs) have shown strong capabilities in generating long, photorealistic videos, but suffer from two key limitations: (i) history forgetti…
BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms
Yunlong Hou, Fengzhuo Zhang, Cunxiao Du +6
Speculative decoding has emerged as a popular method to accelerate the inference of Large Language Models (LLMs) while retaining their superior text generation performance. Previou…