18 citations · 19 across the 4 of their papers we have counts for
5 papers
Acceleration and its Limits in Gaussian Variational Inference
Jaesung R. Park, Uijeong Jang, Yifan Chen
We study whether the classical momentum-based acceleration that improves the condition-number dependence of gradient descent from to for strongly convex, smooth object…
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…
Numerical Analysis of HiPPO-LegS ODE for Deep State Space Models
Jaesung R. Park, Jaewook J. Suh, Youngjoon Hong +1
In deep learning, the recently introduced state space models utilize HiPPO (High-order Polynomial Projection Operators) memory units to approximate continuous-time trajectories of…
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…
Multimodal Knowledge Alignment with Reinforcement Learning
Youngjae Yu, Jiwan Chung, Heeseung Yun +8
Large language models readily adapt to novel settings, even without task-specific training data. Can their zero-shot capacity be extended to multimodal inputs? In this work, we pro…