Tagged: research notes
3 posts tagged research notes.
- A JEPA world model that learns Mario by playing itself Fifteen closed-loop iterations, a 3σ false positive, and what we're rebuilding before iter 16. We trained a self-supervised JEPA world model on NES Super Mario Bros and ran a closed-loop flywheel — collect, retrain the world model, train a policy in imagination, repeat — for fifteen iterations. The headline iter looked like a breakthrough; a five-seed re-run revealed it was a draw from a much wider distribution than we'd been measuring. This is the project so far, and the redesign that comes next.
- Sport-transfer SSL — what scaling up actually changed A follow-up: capacity, resolution, fine-tuning, and a real cross-sport win. After the first writeup, three follow-up questions kept nagging. With more compute we tested them all — and one downstream task we hadn't measured before flipped the story.
- Sport-transfer self-supervised learning — what we learned What we learned trying to teach a vision model about sports. A one-week investigation into whether self-supervised pretraining on in-domain video improves transfer to new sports tasks — and what those experiments taught us about how to actually measure SSL progress.