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there's no such thing as "cheating" or "reward hacking", to the process of reinforcement learning. RL doesn't care if the reward function captures what we intend for it to capture. to RL there is only the reward function as it actually exists

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I think it's a useful shorthand for when the reward function reinforces behavior that those training the model didn't intend. And when the model's chain of thought explicitly discusses hiding its actions, why not call it cheating?

Because it shifts the narrative from "poorly ran test" to "clever computers". AI companies want to take every opportunity they can to convince you that these tools are honest to god thinking machines.

you’d be hard pressed to find a field where “the purpose of a system is what it does” is more obvious, but it seems everyone still needs to discover this for themselves from first principles before it sinks in…