Summary

Reproducing a result from recent work, we study a Gemma 3 12B instance trained to take risky or safe options; the model can then report its own risk tolerance. We find that:

Applying LoRA to a single MLP is enough to reproduce the behavior
- The single LoRA layer learns a single additive steering vector.
- The vector has high cosine similarity with safe/risky words in the unembedding matrix.
We can train just the steering vector, no LoRA needed.
- The steering vector has ~0.5 cosine sim with the LoRA vector learned, but does not seem as interpretable in the unembedding matrix
The layers at which steering works for behavior questions vs. awareness questions seem to be roughly the same. This might imply that the mechanisms are the same as well, that is, there is no separate "awareness mechanism."
Risk backdoors are replicated with a single LoRA layer [...]