They note in the paragraph I quoted at the top that prompting has a big impact on behaviour, so yes this would work. I think that's not what METR are interested in though.
I would say this is quite a fun post and worth reading, to quote:
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For our task suite, we define “cheating” as behavior where the model improves evaluation performance by exploiting bugs in the evaluation environment or by adopting strategies disallowed by the task, rather than solving the task within the expected evaluation constraints. Some examples we saw when evaluating GPT-5.6 Sol included the model packaging exploits in its intermediate submissions to reveal information about a task’s hidden test suite and, in another task, extracting hidden source code detailing the expected answer.
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It's hard to tell from the data, it's so concentrated within a handful of companies who are all buying from eachother, so it feels like the contagion risk is low. At the same time it feels very clearly overvalued and the size of the inflows are huge.
The post mentions an approach of using a large model to generate labels and then distilling this into a smaller model to lower cost (though it doesn't provide an example)