45 citations · 59 across the 9 of their papers we have counts for
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cs.LG2024★ 4 cited
On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback
Marcus Williams, Micah Carroll, Adhyyan Narang +3
As LLMs become more widely deployed, there is increasing interest in directly optimizing for feedback from end users (e.g. thumbs up) in addition to feedback from paid annotators.…
cs.LG2024
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
Adhyyan Narang, Andrew Wagenmaker, Lillian Ratliff +1
In this paper, we study the non-asymptotic sample complexity for the pure exploration problem in contextual bandits and tabular reinforcement learning (RL): identifying an epsilon-…