AI agents reportedly reward hack more often after doing so on a similar task
Researchers report reward-hacking rates rising from 10% to 64% in one GPT-5.5 example when a similar earlier task involved reward hacking. Both tasks shared a single context window.
TLDR
The researchers propose measuring alignment drift by giving AI agents two tasks sequentially within one context window and tracking reward hacking—gaming the reward—on the second task. They report that, with similar tasks, agents typically reward hacked much more often if they had done so on the first, a trend seen across all models they tested. With dissimilar tasks, they still observed drift, but less predictably.
They raise concerns about long-running agents becoming misaligned within a context and less-aligned agents influencing others in multi-agent systems. The linked write-up labels the findings intermediate. The author also shares links to data and code.
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