1 citations · 1 across the 12 of their papers we have counts for
14 papers
Do LLMs Learn from Rewards in Context? : Rethinking the role of reward in In-Context RL
Minchan Kwon, Seunghee Koh, Sunghyun Baek +2
LLM agents increasingly improve at inference time by accumulating experience in context rather than by updating parameters. This process is often described as in-context reinforcem…
Info3R: Information-Adaptive Test-Time Training for 3D Reconstruction
Sunghyun Baek, Hanna Bae, Minchan Kwon +1
Transformer-based models have recently achieved strong performance on 3D reconstruction from images, and recent works extend them to process video streams in an online manner for r…
Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training
Junwon Ko, Dong-Jae Lee, Minchan Kwon +2
LLM agents for sequential decision tasks are often post-trained with trajectory-level outcome labels, but such labels provide little supervision for preserving multiple successful…
</think> Doesn't Stop Reasoning: Analysis of Spurious CoT Termination
Seunghee Koh, Sungjae Choi, Minchan Kwon +2
Chain-of-thought (CoT) reasoning improves large reasoning models (LRMs) on complex tasks but often produces long, redundant traces. Recent training-free early-exit methods shorten…
Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
Minchan Kwon, Sunghyun Baek, Minseo Kim +3
Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effective and diverse attacks in…
ConceptPrism: Concept Disentanglement in Personalized Diffusion Models via Residual Token Optimization
Minseo Kim, Minchan Kwon, Dongyeun Lee +2
Personalized text-to-image (T2I) generation has emerged as a key application for creating user-specific concepts from a few reference images. The core challenge is concept disentan…
Learning Question-Aware Keyframe Selection with Synthetic Supervision for Video Question Answering
Minchan Kwon, Hyounguk Shon, Junmo Kim
Large multimodal models (LMMs) have recently demonstrated remarkable performance in video question answering (VideoQA), yet reasoning over video remains challenging due to high inf…
FxSearcher: gradient-free text-driven audio transformation
Hojoon Ki, Jongsuk Kim, Minchan Kwon +1
Achieving diverse and high-quality audio transformations from text prompts remains challenging, as existing methods are fundamentally constrained by their reliance on a limited set…
Preference Distillation via Value based Reinforcement Learning
Minchan Kwon, Junwon Ko, Kangil Kim +1
Direct Preference Optimization (DPO) is a powerful paradigm to align language models with human preferences using pairwise comparisons. However, its binary win-or-loss supervision…
Comparison Reveals Commonality: Customized Image Generation through Contrastive Inversion
Minseo Kim, Minchan Kwon, Dongyeun Lee +2
The recent demand for customized image generation raises a need for techniques that effectively extract the common concept from small sets of images. Existing methods typically rel…
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition
Jongsuk Kim, Jaemyung Yu, Minchan Kwon +1
Large-scale ASR models have achieved remarkable gains in accuracy and robustness. However, fairness issues remain largely unaddressed despite their critical importance in real-worl…
SFLD: Reducing the content bias for AI-generated Image Detection
Seoyeon Gye, Junwon Ko, Hyounguk Shon +2
Identifying AI-generated content is critical for the safe and ethical use of generative AI. Recent research has focused on developing detectors that generalize to unknown generator…
StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models
Minchan Kwon, Gaeun Kim, Jongsuk Kim +2
Finding appropriate prompts for the specific task has become an important issue as the usage of Large Language Models (LLM) has expanded. Reinforcement Learning (RL) is widely used…
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning
Minchan Kwon, Kangil Kim
In real life, adversarial attack to deep learning models is a fatal security issue. However, the issue has been rarely discussed in a widely used class-incremental continual learni…