5 papers
ReCo: Reweighting GRPO Against Distributional Concentration
Junoh Park, Junseo Hwang, Wonguk Cho +1
Group Relative Policy Optimization (GRPO) has become a standard reinforcement learning method for post-training language models. Recent work shows that GRPO can reduce the base mod…
PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection
Junseo Hwang, Wonguk Cho, Taesup Kim
Fine-tuning large foundation models is essential for building expert models tailored to specialized tasks and domains, but fully updating billions of parameters is computationally…
Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models
Wonguk Cho, Seokeon Choi, Debasmit Das +4
Recent advancements in text-to-image diffusion models have enabled the personalization of these models to generate custom images from textual prompts. This paper presents an effici…
Overcoming Data Inequality across Domains with Semi-Supervised Domain Generalization
Jinha Park, Wonguk Cho, Taesup Kim
While there have been considerable advancements in machine learning driven by extensive datasets, a significant disparity still persists in the availability of data across various…
X-PEFT: eXtremely Parameter-Efficient Fine-Tuning for Extreme Multi-Profile Scenarios
Namju Kwak, Taesup Kim
Parameter-efficient fine-tuning (PEFT) techniques, such as adapter tuning, aim to fine-tune a pre-trained language model (PLM) using a minimal number of parameters for a specific t…