Inference-Time Inoculation Against RL-Induced Misalignment
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Reward hacking during RL can induce split personas in models, some of which are highly misaligned. However, RL is very useful for learning capabilities. Thus, a core problem seems to be: how do we retain the capabilities gained through RL without also inducing reward hacking and broader misalignment?Ideally, we could extensively monitor all rollouts during RL (using both humans and AI) to catch and prevent reward hacking. However, this is potentially prohibitively expensive. Could we capture mos
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