Participants had to minimize held-out loss on a fixed FineWeb dataset while staying within a 16 MB artifact limit, including both model weights and training code, and a 10-minute training budget on 8×H100s. We provided a baseline, dataset, and evaluation scripts so participants could fork the repo, improve the model, and submit their results through GitHub.

Over the course of eight weeks, we received more than 2,000 submissions from over 1,000 participants. We were impressed by the technical breadth, creativity, and rule-bending across the submissions, from careful optimizer tuning and quantization work to new modeling ideas and test-time training.

One of the most exciting parts of the challenge was seeing how widely participants used AI coding agents. Agents helped lower the cost of experimentation, made it easier for more people to participate, and changed the pace of the competition. They also created new challenges for submission review, attribution, and scoring.

The challenge also became a meaningful talent discovery surface for us. That was one of our goals for Parameter Golf, and it was a useful signal that open-ended technical challenges can reveal exceptional machine learning taste and persistence.