RL Reward-Hack Auditing for Chinese AI Safety
Source: LessWrong ·
TL;DR: Access to AI safety talent & resources, particularly for labs during training, is sorely lacking in China. We created one of the largest AI safety communities in China , and plan to next start with auditing reward-hacks of unreliable upstream RL environments that domestic data vendors provide to Chinese frontier labs (which induces reward hacking and a whole host of dangerous downstream tendencies). RL tasks are notoriously prone to exploitation by reward-seeking agents (see examples here , here , and here ). In Xiaomi's MiMo v2.6 RL environments that were open-sourced, 2/3 had leaks on
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RL Reward-Hack Auditing for Chinese AI Safety is a article discovery curated from LessWrong. TL;DR: Access to AI safety talent & resources, particularly for labs during training, is sorely lacking in China. We created one of the largest AI safety communities in China …
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