Hi - This is a common concern we’ve been receiving. We discuss some of them in more detail here - IS2RE Leaderboard Concerns.
TLDR - Models trained on the S2EF dataset (trained with the compute you mentioned) that then run a relaxation to get the relaxed energy are currently the best performing approach. Alternatively, training a model on the IS2RE dataset (~250x less data than the S2EF dataset) to directly predict the relaxed energy is something we’re also interested in for compute reasons (direct approaches are 200-400x faster at inference). To address this (not finalized yet), we are leaning towards awarding 2 teams (1) overall best performance, irrespective of the dataset/approach used and (2) the best performance having only trained on the IS2RE dataset (~460k data points). This would allow teams without heavy compute to still compete without being at a significant disadvantage merely due to compute resources.
Let us know if there are any other concerns. We are constantly trying to make the competition as engaging as possible for the community.