We didn't give more weight to recent puzzles. In fact, we trained on pre-2020 data, validated on data from 2020, and evaluated on post-2020 data.
Our model seems to perform well despite this "time generalization" split, but there are a couple instances where it struggled with new words. For example, we got the answer "FAUCI" wrong in a puzzle from May 2021. Even though Fauci was in the news before 2020, I guess he wasn't famous enough to show up in crosswords, and therefore his name wasn't in our training data.
I think evaluating performance by constructor would be really interesting! But we haven't done that.