3 citations · 4 across the 5 of their papers we have counts for
5 papers
Clip-Low Increases Entropy and Clip-High Decreases Entropy in Reinforcement Learning of Large Language Models
Jaesung R. Park, Junsu Kim, Gyeongman Kim +4
Reinforcement learning with verifiable rewards (RLVR) has recently emerged as the leading approach for enhancing the reasoning capabilities of large language models (LLMs). However…
Task Diversity Shortens the ICL Plateau
Jaeyeon Kim, Sehyun Kwon, Joo Young Choi +4
In-context learning (ICL) describes a language model's ability to generate outputs based on a set of input demonstrations and a subsequent query. To understand this remarkable capa…
Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model
Joo Young Choi, Jaesung R. Park, Inkyu Park +3
Current state-of-the-art diffusion models employ U-Net architectures containing convolutional and (qkv) self-attention layers. The U-Net processes images while being conditioned on…
LoRA Training in the NTK Regime has No Spurious Local Minima
Uijeong Jang, Jason D. Lee, Ernest K. Ryu
Low-rank adaptation (LoRA) has become the standard approach for parameter-efficient fine-tuning of large language models (LLM), but our theoretical understanding of LoRA has been l…
Image Clustering Conditioned on Text Criteria
Sehyun Kwon, Jaeseung Park, Minkyu Kim +3
Classical clustering methods do not provide users with direct control of the clustering results, and the clustering results may not be consistent with the relevant criterion that a…