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cs.AI2026
Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach
Chanwoo Park, Ziyang Chen, Asuman Ozdaglar +1
Large language models (LLMs) are increasingly deployed as "agents" for decision-making (DM) in interactive and dynamic environments. Yet, since they were not originally designed fo…
cs.AI2025
MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning
Chanwoo Park, Seungju Han, Xingzhi Guo +3
Leveraging multiple large language models (LLMs) to build collaborative multi-agentic workflows has demonstrated significant potential. However, most previous studies focus on prom…
cs.AI2024
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation
Chanwoo Park, Mingyang Liu, Dingwen Kong +2
Reinforcement learning from human feedback (RLHF) has been an effective technique for aligning AI systems with human values, with remarkable successes in fine-tuning large-language…