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Frontier LLMs can be induced to generate biologically hazardous sequences, with attack success rates reaching up to 100%.
Intrinsic reward signals in unsupervised RL for LLMs inevitably collapse due to sharpening of the model's prior, but external rewards grounded in computational asymmetries offer a path to sustained scaling.
