4 papers · 1 filter
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…
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…