Author

E. Brunskill

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#artificial intelligence Preprint Oct 2026

Mitigating Social Sycophancy via Pluralistic Preference Optimization

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 often than humans do, which can make people overconfident and less willing to repair their relationships after a conflict. Prior work on mitiga...

Stephane Hatgis-Kessell, Myra Cheng, Xiao-Xuan Hou et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Specifying Reward Functions for RL Without Environment Sampling

This work introduces Experience-Free Autonomous Reward Specification (EARS), a method for learning reward functions from preferences without environment interaction and compares EARS to baselines that also enable reward specification without environment interaction--namely, methods that directly prompt an LLM to genera...

Stephane Hatgis-Kessell, W. B. Knox, Emma Brunskill · 1 citation