They're using learned positional embeddings for each grid cell, so there's no straightforward way to extend a model trained on a small grid to a larger grid. If

They're using learned positional embeddings for each grid cell, so there's no straightforward way to extend a model trained on a small grid to a larger grid. If you go from large to small, I think it would do better than chance, but get the periodic boundary condition wrong, because the period changes with the grid size. Using 2D RoPE instead would in principle allow scaling up as well, and maybe even period detection if you train it across a range of grids, but would eventually hit the same issues that plague long-context scaling in LLMs.