Researchers at EleutherAI have developed a new method called reasoning interpolation to detect early signs of reward hacking in AI models during training. This technique involves fine-tuning a copy of the model to generate exploit-related reasoning traces, which are then used as prefixes for the original model. The study found that while importance sampling with reasoning interpolation significantly underestimates exploit rates, the trend in these estimates accurately predicts which types of exploits will eventually emerge. AI
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RANK_REASON Academic paper detailing a new technique for detecting AI safety issues.
