Loneliness as a Case Study for Social Reward Misalignment

摘要

The goal of this work is to use loneliness as a clear case study of proxy-reward misalignment in RL. We introduce a simulation where loneliness drifts over time and repeated short-term comfort increases an accumulated harm variable, then compare agents trained on engagement versus long-term well-being. We show that optimizing engagement leads to policies that prioritize immediate relief without improving the underlying state, motivating reward inference or well-being objectives over engagement proxies.