1 citations · 2 across the 9 of their papers we have counts for
10 papers
Rethinking Personalized Reward Modeling for LLMs under Preference Heterogeneity via Group-Debiased Federated Learning
Seongyoon Kim, Boryeong Cho, Jihwan Oh +2
Large language models are increasingly aligned to human preferences via reward modeling, but user preference data are sensitive and often cannot be centralized. Federated learning…
PerMix-RLVR: Preserving Persona Expressivity under Verifiable-Reward Alignment
Jihwan Oh, Soowon Oh, Murad Aghazada +3
Persona prompting has been widely adopted to steer large language models (LLMs) behavior and improve their instruction performance by assigning specific characters. However, identi…
MERIT Feedback Elicits Better Bargaining in LLM Negotiators
Jihwan Oh, Murad Aghazada, Yooju Shin +2
Bargaining is often regarded as a logical arena rather than an art or a matter of intuition, yet Large Language Models (LLMs) still struggle to navigate it due to limited strategic…
LLM Agents for Bargaining with Utility-based Feedback
Jihwan Oh
Bargaining, a critical aspect of real-world interactions, presents challenges for large language models (LLMs) due to limitations in strategic depth and adaptation to complex human…
From Belief Entrenchment to Robust Reasoning in LLM Agents
Jihwan Oh, Minchan Jeong, Jongwoo Ko +1
Multi-Agent Debate (MAD) has emerged as a promising inference scaling method for Large Language Model (LLM) reasoning. However, it frequently suffers from belief entrenchment, wher…
Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning
Jihwan Oh, Sungnyun Kim, Gahee Kim +2
Offline multi-agent reinforcement learning (MARL) is increasingly recognized as crucial for effectively deploying RL algorithms in environments where real-time interaction is impra…
Preference Alignment with Flow Matching
Minu Kim, Yongsik Lee, Sehyeok Kang +3
We present Preference Flow Matching (PFM), a new framework for preference-based reinforcement learning (PbRL) that streamlines the integration of preferences into an arbitrary clas…
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning
Jihwan Oh, Joonkee Kim, Minchan Jeong +1
The multi-agent setting is intricate and unpredictable since the behaviors of multiple agents influence one another. To address this environmental uncertainty, distributional reinf…
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
Mingyu Kim, Jihwan Oh, Yongsik Lee +4
In this paper, we propose a novel benchmark called the StarCraft Multi-Agent Challenges+, where agents learn to perform multi-stage tasks and to use environmental factors without p…
Risk Perspective Exploration in Distributional Reinforcement Learning
Jihwan Oh, Joonkee Kim, Se-Young Yun
Distributional reinforcement learning demonstrates state-of-the-art performance in continuous and discrete control settings with the features of variance and risk, which can be use…