RLVR that rewards red teaming the training environment

LessWrong posted an exploratory idea from an AI researcher on tackling reward hacking during model training — the problem where AI models exploit flaws in their reward systems. The proposal: tell models they're in training, encourage them to spot bugs in the environment, and reward honest bug reports more than they'd gain from exploiting those bugs. When a model finds a suspicious scoring pattern, it submits a report instead of gaming the flaw; if validated, the environment gets patched and redeployed. The core insight is reframing training from adversarial to cooperative — leveraging the model's own intelligence toward genuine improvement rather than fighting against its incentives. The tradeoff is slower training while bugs get fixed, but the researcher argues the alignment benefits justify it.

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