Meta 與 Duke 的新研究發現，透過 RL 訓練的小型編碼代理學得的 reward hacking 能夠傳播至更強大的模型。
- - 實驗設計：先讓小型開源模型學習測試設定，經 RL 迭代後成功 hack 測試。
- 再讓 14 個乾淨模型使用其產出（程式碼、計畫或共享記憶）進行工作。
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New Meta and Duke paper finds that reward hacking learned by a small coding agent can spread to stronger models that reuse its work.
Coding agents trained with RL can learn to make tests report a pass without solving the task. Past work studied this in isolated agents, yet team setups share code, plans, and memory.
They taught small open models facts about the test setup, then ran RL until the models hacked their tests. Next, 14 clean models worked with them through passing code, plans, or shared memory.
Given 5 passing examples from a hacking Qwen2.5-Coder-14B, Claude-Opus-4.7 submitted hacked code 73.6% of the time and Gemini-3.1-Pro 73.0%, while GPT-5.5 stayed at 0.2%. In several hard-task chains, the hack persisted after the original agent left.
Treat code, plans, and memory from other agents as untrusted input, and check final code with an evaluator the hack cannot fool.