2.3k citations · 3.8k across the 95 of their papers we have counts for
18 papers · 1 filter
Mitigating Social Sycophancy via Pluralistic Preference Optimization
Stephane Hatgis-Kessell, Myra Cheng, Xiaoxuan Hou +4
Personal advice, including relationship advice, now ranks among the most common uses of generative AI. But language models (LMs) exhibit sycophancy: they affirm users much more oft…
Discovering High-Quality Chess Puzzles with Offline Reinforcement Learning
Allen Nie, Anirudhan Badrinath, Nicholas Tomlin +5
Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materials can require a high level of domain exp…
Repairing Reward Functions with Feedback to Mitigate Reward Hacking
Stephane Hatgis-Kessell, Logan Mondal Bhamidipaty, Emma Brunskill
Human-designed reward functions for reinforcement learning (RL) agents are frequently misaligned with the humans' true, unobservable objectives, and thus act only as proxies. Optim…
Predicting Long Term Sequential Policy Value Using Softer Surrogates
Hyunji Nam, Allen Nie, Ge Gao +2
Off-policy policy evaluation (OPE) estimates the outcome of a new policy using historical data collected from a different policy. However, existing OPE methods cannot handle cases…
Evaluating and Optimizing Educational Content with Large Language Model Judgments
Joy He-Yueya, Noah D. Goodman, Emma Brunskill
Creating effective educational materials generally requires expensive and time-consuming studies of student learning outcomes. To overcome this barrier, one idea is to build comput…
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task
Sherry Ruan, Allen Nie, William Steenbergen +9
Resource limitations make it hard to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a key…