7 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…
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…
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…
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…