we doubled the score on an OpenAI benchmark task.
0 models trained. 0 predictions moved in the ranking.
benchmark speedrun any%
The Jigsaw grader turned predictions into 0s and 1s before scoring them. Multiply all 400 predictions by 0.49 and the grader sees 400 zeros.
We removed that one line locally. Both versions scored 1.00.
If your eval uses ROC-AUC, try rescaling the predictions while keeping their order and ties intact. The score should stay put. You can check that before paying for another training run.
github.com/openai/mle-bench/…
Ray’s autoscaler reused one mutable plan for two launches. Separate plans got the launch count back to 1.
Canceling the extra launch still emitted a termination request with an empty instance ID. The proposed patch cancels pending launches locally.
github.com/ray-project/ray/p…



