The problem is that the input is tokenized before the model gets it as input. It does not see the individual letters "t" + "o". It gets one single token, #1462. The word "toe" is another single token, #44579. Maybe over time it could learn from context that inputs that start with #44579 also satisfy the constraint of starting with #1462, but that's a lot of work and it's not going to happen for all combinations of letters.

Perhaps prompting the model to first describe its approach to answering the question. This type of chain-of-thought technique can yield better results.