2.2k citations · 4.1k across the 118 of their papers we have counts for
4 papers · 1 filter
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…
Beyond RLHF and NLHF: Population-Proportional Alignment under an Axiomatic Framework
Kihyun Kim, Jiawei Zhang, Asuman Ozdaglar +1
Conventional preference learning methods often prioritize opinions held more widely when aggregating preferences from multiple evaluators. This may result in policies that are bias…
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…
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…