You've slightly missed the point (for a similar, adjacent point). Chain of thought monitors are a worry even when there's no vector space path from the output at index t to the input at index t+1. Rather, the fear is that if you explicitly penalize some CoT pattern, you're encouraging the network to do whatever it was going to do anyhow, but in a way you can't catch.

Say you've noticed that when your LLM cheats, it usually happens after the string "Hmm, let's try cheating" occurs in the CoT. So you think, hey, let's just apply a -1 reward penalty whenever we see that string! The model learns to keep cheating, but instead of telling you, it now prefaces reward hacking with strings like "Let's make sure we're doing this the way the user intended." Still cheats, but now you can't detect it with a cheap pattern matching alert. Oops.