I don't think there is a general answer here. But here are a couple of considerations:
- networks can get stuck in local optima, so if you initialize it to memorize, it might never find a general solution.
- grokking has shown that with high weight regularization, networks can transition from memorized to general solutions, so it is possible to move from one to the other.
- it probably depends a bit on how exactly you initialize the memorized solution. You can represent lookup tables in different ways and some are much more liked by NNs than others. For examp...
All of the above but in a specific order.
1. Test if the model has components of deceptive capabilities with lots of handholding with behavioral evals and fine-tuning.
2. Test if the model has more general deceptive capabilities (i.e. not just components) with lots of handholding with behavioral evals and fine-tuning.
3. Do less and less handholding for 1 and 2. See if the model still shows deception.
4. Try to understand the inductive biases for deception, i.e. which training methods lead to more strategic deception. Try to answer ques... (read more)