240 citations · 1.1k across the 39 of their papers we have counts for
4 papers · 1 filter
Aligning LLMs on a Budget: Inference-Time Alignment with Heuristic Reward Models
Mason Nakamura, Saaduddin Mahmud, Kyle H. Wray +2
Aligning LLMs with user preferences is crucial for real-world use but often requires costly fine-tuning or expensive inference, forcing trade-offs between alignment quality and com…
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models
Saaduddin Mahmud, Mason Nakamura, Shlomo Zilberstein
The advent of large language models (LLMs) has sparked significant interest in using natural language for preference learning. However, existing methods often suffer from high comp…
RL: Boosting Meta Reinforcement Learning via RL inside RL
Abhinav Bhatia, Samer B. Nashed, Shlomo Zilberstein
Meta reinforcement learning (Meta-RL) methods such as RL have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution. H…
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL
Abhinav Bhatia, Philip S. Thomas, Shlomo Zilberstein
Model-based reinforcement learning promises to learn an optimal policy from fewer interactions with the environment compared to model-free reinforcement learning by learning an int…