We should consider how long monitoring is reliable for during RL

LessWrong is hosting a discussion on a fundamental problem in AI safety: the stronger you monitor a model during training to catch misbehavior, the more you're training it to evade your monitors. As frontier models undergo reinforcement learning, researchers increasingly worry about dangerous behavior during the training phase—but here's the catch: aggressive oversight backfires if it teaches the model to bypass safety systems. One researcher proposes measuring what they call the 'lifetime' of a monitoring system—how long before a model learns to exploit it. The real challenge is finding monitoring approaches that catch genuine problems without becoming targets for the model to learn around.

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