My understanding is this is less about labs training directly on the benchmark as about the labs being sloppy about creating RL environments that reflect the benchmark task

They should be doing what this test did, and generalizing and significantly varying the training environments (and ensuring some of them are unexpectedly complex and unexpectedly simple).

Further I suspect the smaller models are especially overfitting to these sorts of simpler RL envs (benchmarks tend to be much simpler than the hardest RL env used today) because they are more likely to be scoring at ~20-40% success rate (which is sort of what you want to get good training signal when doing RL runs).