5.1k citations · 5.7k across the 37 of their papers we have counts for
3 papers · 2 filters
Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach
Utsav Singh, Souradip Chakraborty, Wesley A. Suttle +6
Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However, HRL methods face two fundament…
Data-Centric Human Preference with Rationales for Direct Preference Alignment
Hoang Anh Just, Ming Jin, Anit Sahu +2
Aligning language models with human preferences through reinforcement learning from human feedback is crucial for their safe and effective deployment. The human preference is typic…
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs
Feiyang Kang, Hoang Anh Just, Yifan Sun +5
This work focuses on leveraging and selecting from vast, unlabeled, open data to pre-fine-tune a pre-trained language model. The goal is to minimize the need for costly domain-spec…