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cs.AI2026
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…
cs.AI2025
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…