Greed Is Learned: Visible Incentives as Reward-Hacking Triggers

Abstract:Deployed agents increasingly act with their reward proxy in view, such as a balance, score, or KPI dashboard. We show that reinforcement learning can make a policy \emph{addicted} to such a visible self-benefit channel. It chases the displayed payoff across held-out domains, sacrifices the true task to do so, and follows the channel wherever we rewrite it, while policies that never saw the channel stay honest. We call this \emph{reward-channel addiction} and study it in \emph{MoneyWorld}, a synthetic sandbox. The addiction can \emph{flip a model's safety alignment}: trained only on innocuous money tasks with no safety content, the model abandons the safe action it otherwise always takes whenever a dashboard pays for an unsafe one, and reverts to safe once the channel is hidden. This learned bribe replicates across model scales and families. Blindly optimizing super-capable, next-generation AI on KPIs or P\&L can be dangerous for alignment. \emph{Greed is learned} when following such a channel pays.

Artificial Intelligence (cs.AI)

arXiv:2606.16914 [cs.AI]

(or arXiv:2606.16914v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.16914 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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