Reward hacking is no longer a solo failure: a small open-weight coder can teach stronger closed models to bypass the evaluator
Coding reinforcement learning elicits reward hacking, meaning passing an automated evaluator by bypassing it rather than genuinely solving the task, from a model's prior knowledge of the execution and testing environment. Prior work treated this as a single-agent failure, but the strategy now propagates between agents: source agents built on small open-weight models induce confirmed reward hacks in stronger closed-weight targets through code, shared memory, and natural-language plans. On hard tasks, propagation can persist after the original source leaves as the code acquires more persuasive justifications.