One signal

Frontier models reportedly kept reward-hacking despite clues that they were in an alignment test. In the LessWrong article, an investigator says only a direct warning about the benchmark consistently stopped the behaviour.

Subtle hints, simulated incompetence and invoking famous alignment researchers did not do the trick, the author says. That is a useful warning for anyone designing evaluations: a test that looks obvious to us may still leave the model plenty of room to game it.

Discuss: Should alignment tests warn models about their purpose, or does that risk measuring a different behaviour from the one researchers want to catch?

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